PE firms way would benefit multibusiness enterprise

PE firms way would benefit multibusiness enterprise

Successful PE firms model practices that would benefit any multibusiness enterprise—as well as some that break the public-company mold.

In many respects, successful private-equity (PE) firms seem to defy economic logic. They acquire most of their businesses through some form of auction, where competitive bidding drives prices above what other potential buyers are willing to pay. Because they manage portfolios of discrete businesses, their acquisitions rarely reap substantial synergies. Their ability to survive, let alone thrive, depends on sustaining returns that attract limited partners to reinvest every few years. And unlike traditionally organized public companies, PE firms can’t underperform for very long, because their track records directly affect their ability to tap into capital markets.

Yet a number of prominent private-equity firms have succeeded for decades, earning healthy returns for investors and founders alike. So it’s not surprising that some public-company managers would look in that direction for new models to address their own myriad challenges—around aspects of governance, operations, and active ownership, among other things.1The way private-equity firms manage strategic planning, for example, offers lessons that might help public companies adapt to an environment marked by heightened shareholder pressure for performance and a fast-paced business cycle.

In our experience, successful private-equity firms excel at some practices that public companies should—but often don’t. These include detaching themselves from the tyranny of quarterly-earnings guidance, deploying highly disciplined business-unit strategies, and developing a competitive advantage in M&A. We believe many public companies would benefit from applying a private equity–like approach more aggressively in these areas, even by going to lengths that might seem unorthodox.

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Don’t be tyrannized by the short term

Private equity’s most powerful advantage may simply be that it is private. These firms can restructure and invest for the future while avoiding the glare of quarterly analysts’ calls and the business media. They can also communicate more intimately with a much smaller investment community, so they don’t broadcast their strategies and growth advantages to competitors. Our research shows that public-company managers can also gain shareholder support for long-term programs by communicating convincingly and making the right progress metrics clear to the investment community.

In the first 100 days after an acquisition, some successful PE firms explicitly collaborate with the new portfolio company during an intensive planning process. Over this period, management and the board develop a five- to seven-year plan, agreeing on new markets, channels, or products; assessing the capital needed to execute these initiatives; and developing an explicit set of new metrics and corresponding management incentives. In addition, they identify tactical near-term moves to build positive momentum from the deal’s most readily apparent benefits.

Such efforts require a highly disciplined, rigorous emphasis on metrics that reflect longer-term value, like cash flow, rather than short-term ones, like earnings per share (EPS). Many private-equity firms separate the financing of a business from its operating performance, which they get management teams to focus on by using cash flow–based measures, such as earnings before interest, taxes, depreciation, and amortization (EBITDA) and free cash flow. EPS reflects nonoperating factors (such as interest and tax expenses) that rely on a deal’s structure, but EBITDA depends more on operating performance. Free cash flow also takes into account the capital expenditures and additional working capital required to generate profits; EPS does not.

During the 100-day planning process, private-equity firms are more active than public companies in considering the furthest horizons of strategic planning. Public companies often focus on nearer-term objectives, including existing baseline products and emerging product lines, though longer-term bets can help to create significant longer-term value. Typically, private-equity firms more actively identify and emphasize strategic planning’s third horizon—including new markets and products—and diligently make tactical bets on it. For example, when PE firm Clayton Dubilier & Rice (CD&R) acquired PharMEDium for $900 million, in 2014, it hadn’t previously invested in outpatient care. But managers identified this as a major growth opportunity and made a calculated bet that paid off handsomely. CD&R ultimately sold the business for $2.6 billion.

Public companies could emulate much of this. Quarterly earnings can’t be ignored, but long-term shareholder value depends heavily on the generation of free cash and on the third horizon of future growth trajectories. Public companies should also explore the intensive 100-day planning process PE firms put in place after acquisitions, whether every other year or after the transition to a new leadership team.

Create disciplined business-unit strategies

A multibusiness company is the sum of its parts: if strategies for the underlying units aren’t focused and robust, neither will the overall picture. Success requires picking winners and backing them fully—something that often eludes public companies looking for the next new thing. Indeed, most of them pass only three out of ten tests of business-unit strategy.2Although financial theory suggests that capital should always be available for attractive investments, public companies that are constrained, for example, by their EPS commitments to Wall Street or by planned dividends often face intense competition for internal resources. Too often, they spread those resources thinly across business units. The right strategy means little if it isn’t fully resourced.

Private-equity firms don’t plan strategy around business units, but their investment theses for portfolio companies amount to the same thing. They’re a plan for investing across a portfolio of businesses, basing the allocation of capital on ROIC relative to risk, and explicit plans for creating incremental value in each business. PE firms do focus less than public ones on the strategic fit of companies in their portfolios—a tech company in a portfolio of heavy-industry businesses wouldn’t be a concern because they’re managed separately. But the portfolio-management objectives and disciplines ought to be similar. Both public companies and PE firms should evaluate a similar set of expansion options to assess market context, potential returns, and potential risks.

PE firms develop, monitor, and act upon performance metrics built around an investment thesis. That’s in sharp contrast with the one-size-fits-all metrics public companies often use to evaluate diverse business units—an approach that overlooks differences among them resulting from their position in the investment cycle, their prospective roles in the overall portfolio, and the different market and competitive contexts in which they operate. Although tailoring metrics to reflect these differences is hard work, it gives corporate management a much clearer picture of each unit’s progress.

Public companies could go further. Unlike PE firms, for example, they traditionally manage the balance sheets of a business unit against the needs of the enterprise as a whole. But should they always do so? Instead of divesting a slow-growing but cash-generating legacy business unit, should they have it issue its own nonrecourse debt? This would save the tax and transaction costs of divestiture, and potentially preserve additional upside. Would it make sense to bring outside capital into a high-risk emerging business unit—as Google X (now known as X) did for some of its nascent healthcare ventures? This approach would help investors to see the long-term value of such units, which would be more directly exposed to the discipline of the capital markets.

In addition, public companies could emulate the governance of private-equity firms at the business-unit level, where each portfolio company has its own board of directors. These boards are generally controlled at the firm level, but they are often supplemented by knowledgeable and senior outsiders with a meaningful equity stake. Since board activities focus on only one business unit, they can effectively surface, grasp, and debate the critical strategic, organizational, and operational issues it faces. While creating true governance boards for business units isn’t a realistic option for a public company, nothing prevents it from appointing advisory boards, with incentives based on the creation of value at the specific business units they oversee. In fact, freedom from formal governance responsibilities may make such boards more effective, allowing them to spend significant amounts of time on strategy and on developing management.

Finally, public companies could do more to compensate business-unit managers based on their own results. Compensation for private-equity fund managers typically reflects the results of the fund as a whole, but the pay of management teams at portfolio companies strictly reflects their own company’s value creation. This means that portfolio company executives in a lagging business can’t hope to be carried along by strong results at the fund level. It also means that executives in high-performing portfolio companies won’t be affected by the poor performance of entities over which they have no influence. This is a powerful motivator in both directions.

Could it make sense, for example, for multibusiness public companies to link incentive compensation for business-unit managers not to traditional stock options but rather to “phantom” stocks3that reflect changes in the intrinsic value of their business units? That would be counterproductive where businesses are highly interdependent, but in many cases at least some parts of a company operate more independently. And such an approach could generate the kind of entrepreneurial focus on value that private-equity firms get from the management teams of their portfolio companies. In the 1980s, Genzyme, for example, pioneered many tracking stocks for specific business units, and John Malone used them recently for those of conglomerate Liberty Media.

Develop M&A capabilities as a competitive advantage

Among public, nonbanking companies, those that routinely acquire and integrate clearly outperform their peers.4That fact should make unearthing, closing, and extracting value from attractive acquisitions a functional skill—like the effectiveness of the sales force, manufacturing, or R&D. Many public companies don’t treat it that way, but the best private-equity firms do, building and institutionalizing M&A skills as a competitive advantage.

Public companies that do behave like successful PE firms engage in M&A around a handful of explicit themes, supported by both organic and acquired assets to meet specific objectives. Achieving this competitive advantage calls for proactively identifying attractive strategic targets, often outside banker-led deal processes. It calls for managing a reputation as a bold, focused acquirer that can offer real mentorship and distinctive capabilities. And it calls for effective commercial and financial diligence based on the detailed information available to acquirers after signing letters of intent. Other requirements include reassessing synergy targets, adjusting them as appropriate to provide a margin of safety, and being highly disciplined about the price paid for acquisitions, to ensure accretion.5Most public companies seek to develop these skills, but many don’t dedicate enough time or resources.

http://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/what-private-equity-strategy-planners-can-teach-public-companies?cid=other-eml-alt-mip-mck-oth-1610

Comp and skills of the 7 most promising finance jobs

Comp and skills of the 7 most promising finance jobs

When it comes to careers, “finance” is a sweeping term.

So before you hit Wall Street, you’ll need to figure out which role is right for you.

LinkedIn broke down the top finance jobs of 2017, based on high median salaries, job openings, year-over-year-growth, and potential for promotion.

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Here are LinkedIn’s top seven picks:

1. Financial analyst

Median base salary: $62,000

Job openings: 1,700+

Top skills: Financial analysis, financial reporting, accounting, Microsoft Excel, financial modeling

2. Underwriting manager

Median base salary: $102,000

Job openings: 100+

Top skills: Underwriting, general insurance, commercial insurance, property and casual insurance, liability

3. Quantitative analyst

Median base salary: $105,000

Job openings: 200+

Top skills: Quantitative finance, derivatives, visual basic for applications, quantitative analytics, Matlab

4. Scrum master

Median base salary: $100,000

Job openings: 500+

Top skills: Scrum, Agile methodologies, Agile project management, software development, requirements analysis

5. Data analyst

Median base salary: $63,000

Job openings: 1,000+

Top skills: SQL, SAS, statistics, databases, Microsoft Excel, data mining

6. Product manager

Median base salary: $99,000

Job openings: 500+

Top skills: Product management, product marketing, product development, competitive analysis, product launch

7. Credit analyst

Median base salary: $52,500

Job openings: 400+

Top skills: Financial analysis, credit risk, credit, banking, loans

http://www.businessinsider.com/best-finance-jobs-of-2017-2017-2

Swiss unstable about corporate tax reforms

Swiss unstable about corporate tax reforms

Bern must rethink rules after 60% dismiss proposal to cut overall rates in referendum

Switzerland a more UN-stable and less competitive country … a growing mood against establishment and global corporations special tax regime …no more discretionary and advantageous rules. Www.maltaway.com for a very stable country and a fully OECD and EU compliant jurisdiction

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Switzerland’s attempts to overhaul its corporate tax regime have suffered a setback after voters decisively rejected reforms to bring the country’s practices in line with international standards. The government had hoped to secure approval for changes that would keep corporate tax rates globally competitive while abolishing special treatment for many multinational companies. In a referendum on Sunday, however, the plan was rejected by 59.1 per cent of voters — a much larger margin of defeat than opinion polls had suggested. Bern and the Swiss cantons must now rethink the proposals in the face of threats that important trading partners could take retaliatory action. The defeat is a blow for the business lobby in Switzerland, which fears damaging uncertainty over future corporate tax bills. The defeat meant Switzerland would no longer fulfil its promises to abolish special privileges by 2019, said Ueli Maurer, finance minister. He feared companies would quit Switzerland, or no longer move to the country as a result of the uncertainty created by Sunday’s vote. Read more Luxembourg expects more companies to leave over tax scrutiny Finance minister expects some international groups to follow lead set by McDonald’s Switzerland faced increasing international tax competition — including possibly from the UK, “so we don’t have much room for manoeuvre,” Mr Maurer warned. Given the scale of the government’s defeat, he expected it would take at least a year to draw up a revised reform package — with legislative approval following afterwards. The result had created “great insecurity”, according to Swissmem, the Swiss industry association. A revised reform package was “urgently needed” to preserve the country’s competitiveness. Ahead of the vote, Switzerland was warned that failure to dismantle practices considered harmful by other countries could result in an international backlash. “Switzerland’s partners expect that it will implement its commitments in a reasonable timeframe,” Pascal Saint-Amans, head of tax at the Paris-based OECD, said. The unexpectedly clear No vote suggested that the global anti-establishment mood had reached Switzerland. The reforms had been backed overwhelmingly by the two chambers of the Swiss parliament as well as the government, with opposition largely from leftwing parties. Since the second world war, multinational companies have helped the small Alpine economy become one of the world’s most successful economies. Under the reform plans, the country’s 26 cantons would have continued to compete to offer companies the most favourable tax rates, but multinationals would have paid the same rates as other businesses. To avoid imposing much larger bills on multinationals, the cantons announced plans to slash corporate tax rates for other companies, while the federal government in Bern said it would help fill shortfalls in tax revenues. The canton of Geneva, for example, planned to cut its general corporate tax rate from about 24 per cent to 13.5 per cent. Opponents led by the Swiss Social Democratic party argued, however, that the new system would have been too generous to business and led to large gaps in cantons’ budgets, which in turn would have hit public services. Further alienating voters was a complex system of internationally acceptable tax reliefs that would have been available under the new system, for instance for research and development or income from patents and on shareholders’ equity. Critics argued they would have simply boosted the income of tax advisers, lawyers and shareholders. Opponents also argue the reforms could be modified relatively easily — a point disputed by supporters, who said that the package took years of careful negotiation between the cantons and federal government. What happens next is unclear. The cantons could still push ahead with corporate tax changes that bring them into line with international standards — but without help from the federal government. Jan Schüpbach, economist at Credit Suisse, said: “Switzerland has promised to abolish the special status [of many multinationals], so we think retaliation is unlikely in the short term, if the government comes up soon with a Plan B.” “What actually happens will depend on whether there is international pressure on companies, and the cantons feel obliged to offer them a tax regime which is internationally acceptable. But the leeway for cantons to lower taxes is now less because they won’t get the extra federal funding.” Supporters of the reforms have argued that by securing Switzerland’s competitiveness, they would boost jobs and investment. Critics, however, have said that multinationals like Switzerland because of other factors — including its high quality transport infrastructure and skilled workforce.

https://www.ft.com/content/92a6ec56-f113-11e6-95ee-f14e55513608

The Real Cost of an MBA and US ranking

The Real Cost of an MBA

To figure out the true price of a business degree, you have to factor in the opportunity cost.

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After working for eight years in accounting and finance, most of it at PwC, Tully Brown knew it was time to deepen his business skills. So he did what a lot of young professionals in his shoes do: He went for an MBA.

For Brown, who enrolled at Emory University’s Goizueta Business School in 2015, the biggest cost wasn’t tuition, fees, and housing. It was the six-figure job he gave up to attend school full-time. “Being a numbers guy, I actually modeled it out,” he says. “I looked at what would happen, because there was the possibility I’d end up leaving and making the same that I did before going in. I decided it was worth the risk.”

The typical incoming MBA student at Emory earns $67,000 the year prior to enrollment. Multiply that by the length of a two-year MBA program, then add to it Emory’s cost of attendance, and you get $296,536. Using the forgone salaries reported by thousands of recent graduates as part of Bloomberg Businessweek’s annual ranking of the top full-time MBA programs, we were able to create a similar “real” cost figure for several of the schools on our list. Stanford Graduate School of Business had the highest full-freight cost, more than $434,000, because those in its MBA program earned more when they enrolled.

Each year, we rank business schools by polling students on topics such as academics, career services, and campus climate. We also ask employers about the skills they seek in MBA hires and which schools best prepare their graduates. Starting in 2015, we began surveying alumni, asking them how well their degrees had delivered on the promise of a fulfilling and profitable career.

For the second year in a row, Harvard Business School came out on top—and this time by a wider margin. HBS was rated No. 1 by the more than 1,000 corporate recruiters we surveyed and No. 3 among alumni. Competition for the No. 2 spot overall was particularly close this year, with Stanford edging out Duke University’s Fuqua School of Business by less than one index point.

Unlike undergraduate students who are often giving up low-paying jobs to return to school, MBA students are typically in their late 20s or early 30s and leaving well-paid positions. That makes calculating the opportunity cost even more significant for MBA seekers. Because Bloomberg Businessweek surveys B-school alumni on the salaries they drew before enrolling in a full-time MBA program, we were able to calculate the true cost of the degree. Our formula isn’t perfect. It includes the loss of two years’ worth of wages, even though some students don’t go a full 24 months without working, and some have well-paid internships during the summer break. In addition, we weren’t able to factor in financial aid or scholarships.

Schools’ cost-of-attendance breakdowns don’t reflect the income students give up during their time in school. B-school officials say MBA applicants should definitely add that cost to tuition and other expenses when calculating a projected return on their investment. It’s “a large number, there is no doubt about it,” says Douglas Skinner, interim dean of the University of Chicago Booth School of Business, noting that some students may need to take out loans to also cover the lost wages. Still, Skinner stressed that prospective students need to understand that the knowledge obtained through the degree will help boost their earning power not just immediately after graduation but throughout their careers.

That’s why Brown says he didn’t mind leaving a well-paying job at a community bank in Atlanta to enroll at Emory. “It’s hard to know in 10 years whether I get something I want because I have an MBA,” says the 33-year-old who took out $53,000 in student loans, partly so he wouldn’t have to miss out on student networking opportunities like international trips. “But I decided I wouldn’t want to wonder in 10 years if not having the MBA was holding me back—because then it might be too late to do it.”

Although Brown chose the one-year, or fast-track, MBA at Emory, he still went without income for about 15 months. As he’d hoped, he landed a job at an investment bank, where he’s earning more than he did before.

Data from our surveys show that the typical graduate at the schools we ranked earned a positive return on her investment. Alums of the 87 schools ranked this year earned a median $50,000 prior to starting an MBA program and saw that salary rise 80 percent upon graduation. And six to eight years on, the median salary hovered around $145,000.

Students who enroll in MBA programs are usually further along in their lives and therefore more likely to be married. That can make the degree even more pricey. Spouses who quit their jobs and move to such places as Hanover, N.H. (home of Dartmouth College) or New Haven (Yale) to accompany an MBA student may face poor employment prospects.

That was the case for James Mansour, a recent graduate of Texas A&M University’s Mays Business School, and his wife. They relocated from the Washington, D.C., area, where he worked as a consultant and she as a dental hygienist, pulling in a combined $160,000 a year. After the move the couple learned that they were expecting a child and that she’d have to get relicensed to work in Texas. Fortunately, the couple was able to get by on savings by cutting out vacations, dining out, and shopping. Mansour, who graduated in December, now works at Delta Air Lines in Atlanta, and has a higher salary than he did before he went for his degree.

The typical 2016 MBA graduate from Dartmouth College’s Tuck School of Business earned $80,000 before enrollment. So if a student were to forgo that income for two years and pay the full cost of attendance, his or her real cost would amount to $360,100. Matthew Slaughter, dean of Tuck, says that while most graduates receive a large earnings boost after graduating, that isn’t always the case in the short term. “For a lot of programs like ours, there are a lot of people career-switching,” he says. “So that might entail giving up industry- or company-specific capital to really build capital in a totally different area that’s more satisfying.”

Katherine Earle graduated from Stanford’s MBA program this spring. She walked away from a well-paying position in the technology sector to go back to school. Still, she says she’s confident she made the right choice. “I’m already making more now,” says Earle, “and that increase will hopefully continue to compound itself, partly because of the inherent value of the degree but more likely because I had the opportunity to study my personal strengths and weaknesses and career interests at school.”

http://www.bloomberg.com/news/articles/2016-11-16/real-cost-of-an-mba

Teaching an Algorithm to Understand Right and Wrong

Teaching an Algorithm to Understand Right and Wrong

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In his Nicomachean Ethics, Aristotle states that it is a fact that “all knowledge and every pursuit aims at some good,” but then continues, “What then do we mean by the good?” That, in essence, encapsulates the ethical dilemma. We all agree that we should be good and just, but it’s much harder to decide what that entails.

Since Aristotle’s time, the questions he raised have been continually discussed and debated. From the works of great philosophers like Kant, Bentham, and Rawls to modern-day cocktail parties and late-night dorm room bull sessions, the issues are endlessly mulled over and argued about but never come to a satisfying conclusion.

Today, as we enter a “cognitive era” of thinking machines, the problem of what should guide our actions is gaining newfound importance. If we find it so difficult to denote the principles by which a person should act justly and wisely, then how are we to encode them within the artificial intelligences we are creating? It is a question that we need to come up with answers for soon.

Designing a Learning Environment

Every parent worries about what influences their children are exposed to. What TV shows are they watching? What video games are they playing? Are they hanging out with the wrong crowd at school? We try not to overly shelter our kids because we want them to learn about the world, but we don’t want to expose them to too much before they have the maturity to process it.

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In artificial intelligence, these influences are called a “machine learning corpus.” For example, if you want to teach an algorithm to recognize cats, you expose it to thousands of pictures of cats and things that are not cats. Eventually, it figures out how to tell the difference between, say, a cat and a dog. Much as with human beings, it is through learning from these experiences that algorithms become useful.

However, the process can go horribly awry, as in the case of Microsoft’s Tay, a Twitter bot that the company unleashed on the microblogging platform. In under a day, Tay went from being friendly and casual (“Humans are super cool”) to downright scary (“Hitler was right and I hate Jews”). It was profoundly disturbing.

Francesca Rossi, an AI researcher at IBM, points out that we often encode principles regarding influences into societal norms, such as what age a child needs to be to watch an R-rated movie or whether they should learn evolution in school. “We need to decide to what extent the legal principles that we use to regulate humans can be used for machines,” she told me.

However, in some cases algorithms can alert us to bias in our society that we might not have been aware of, such as when we Google “grandma” and see only white faces. “There is a great potential for machines to alert us to bias,” Rossi notes. “We need to not only train our algorithms but also be open to the possibility that they can teach us about ourselves.”

Unraveling Ethical Dilemmas

One thought experiment that has puzzled ethicists for decades is the trolley problem. Imagine you see a trolley barreling down the tracks and it’s about to run over five people. The only way to save them is to pull a lever to switch the trolley to a different set of tracks, but if you do that, one person standing on the other tracks will be killed. What should you do?

Ethical systems based on moral principles, such as Kant’s Categorical Imperative (act only according to that maxim whereby you can, at the same time, will that it should become a universal law) or Asimov’s first law (a robot may not injure a human being or, through inaction, allow a human being to come to harm) are thoroughly unhelpful here.

Another alternative would be to adopt the utilitarian principle and simply do what results in the most good or the least harm. Then it would be clear that you should kill the one person to save the five. However, the idea of killing somebody intentionally is troublesome, to say the least. While we do apply the principle in some limited cases, such as in the case of a Secret Service officer’s duty to protect the president, those are rare exceptions.

The rise of artificial intelligence is forcing us to take abstract ethical dilemmas much more seriously because we need to code in moral principles concretely. Should a self-driving car risk killing its passenger to save a pedestrian? To what extent should a drone take into account the risk of collateral damage when killing a terrorist? Should robots make life-or-death decisions about humans at all? We will have to make concrete decisions about what we will leave up to humans and what we will encode into software.

These are tough questions, but IBM’s Rossi points out that machines may be able to help us with them. Aristotle’s teachings, often referred to as virtue ethics, emphasize that we need to learn the meaning of ethical virtues, such as wisdom, justice, and prudence. So it is possible that a powerful machine learning system could provide us with new insights.

Cultural Norms vs. Moral Values

Another issue that we will have to contend with is that we will have to decide not only what ethical principles to encode in artificial intelligences but also how they are coded. As noted above, for the most part, “Thou shalt not kill” is a strict principle. Other than a few rare cases, such as the Secret Service or a soldier, it’s more like a preference that is greatly affected by context.

There is often much confusion about what is truly a morale principle and what is merely a cultural norm. In many cases, as with LGBT rights, societal judgments with respect to morality change over time. In others, such as teaching creationism in schools or allowing the sale of alcohol, we find it reasonable to let different communities make their own choices. 

What makes one thing a moral value and another a cultural norm? Well, that’s a tough question for even the most-lauded human ethicists, but we will need to code those decisions into our algorithms. In some cases, there will be strict principles; in others, merely preferences based on context. For some tasks, algorithms will need to be coded differently according to what jurisdiction they operate in.

The issue becomes especially thorny when algorithms have to make decisions according to conflicting professional norms, such as in medical care. How much should cost be taken into account when regarding medical decisions? Should insurance companies have a say in how the algorithms are coded?

This is not, of course, a completely new problem. For example, firms operating in the U.S. need to abide by GAAP accounting standards, which rely on strict rules, while those operating in Europe follow IFRS accounting standards, which are driven by broad principles. We will likely end up with a similar situation with regard to many ethical principles in artificial intelligences.

Setting a Higher Standard

Most AI experts I’ve spoken to think that we will need to set higher moral standards for artificial intelligences than we do for humans. We do not, as a matter of course, expect people to supply a list of influences and an accounting for their logic for every decision they make, unless something goes horribly wrong. But we will require such transparency from machines.

“With another human, we often assume that they have similar common-sense reasoning capabilities and ethical standards. That’s not true of machines, so we need to hold them to a higher standard,” Rossi says. Josh Sutton, global head, data and artificial intelligence, at Publicis.Sapient, agrees and argues that both the logical trail and the learning corpus that lead to machine decisions need to be made available for examination.

However, Sutton sees how we might also opt for less transparency in some situations. For example, we may feel more comfortable with algorithms that make use of our behavioral and geolocation data but don’t let humans access that data. Humans, after all, can always be tempted. Machines are better at following strict parameters.

Clearly, these issues need further thought and discussion. Major industry players, such as Google, IBM, Amazon, and Facebook, recently set up a partnership to create an open platform between leading AI companies and stakeholders in academia, government, and industry to advance understanding and promote best practices. Yet that is merely a starting point.

As pervasive as artificial intelligence is set to become in the near future, the responsibility rests with society as a whole. Put simply, we need to take the standards by which artificial intelligences will operate just as seriously as those that govern how our political systems operate and how are children are educated.

It is a responsibility that we cannot shirk.

https://hbr.org/2016/11/teaching-an-algorithm-to-understand-right-and-wrong

Index Funds Are Fueling Out-of-Whack CEO Pay Packages

Index Funds Are Fueling Out-of-Whack CEO Pay Packages

CEOs get paid handsomely. The pay of top managers has risen faster than those of other star earners. Often they’re paid generously even as the firms they head underperform relative to their peers.

Such performance-insensitive pay packages seem to defy both common sense and established economic theory on optimal incentives. Top management compensation packages guarantee a high level of pay, but are often only weakly linked to the performance of the firm relative to its industry competitors. Why, then, do company boards and shareholders of most firms approve those packages?

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We propose an answer to that question in our new research paper. By endorsing performance-insensitive compensation packages, broadly diversified investors are indeed incentivizing CEOs for good performance. Except that the performance that they’re rewarding is industry performance, not company performance. Why? These days, most firms’ most powerful shareholders tend to benefit more from the performance of the entire industry than the performance of an individual firm.

To understand this new explanation for seemingly exorbitant CEO pay, it’s important to understand a recent, fundamental shift in the ownership of U.S. public companies. Nowadays, the same handful of large, diversified asset management companies controls a significant proportion of US corporations.

For example, BlackRock is the largest shareholder of about one in five publicly-listed US corporations, often including the largest competitors in the same industry. Similarly, Fidelity is the largest shareholder of one in ten public companies and frequently owns stakes of 10-15% or more. Even Bill Gates’ ownership of about 5% of Microsoft’s stock is small compared to the top five diversified institutional owners’ holdings, which amount to more than 23%.

Magnifying their already large individual power, large asset managers also appear to coordinate many corporate governance activities, including those regarding compensation. The potential of coordination among BlackRock, Vanguard, and State Street is particularly potent given that their combined power makes themthe largest shareholder of 88% of all S&P 500 firms.

This sweeping development known as “common ownership” – the same firms owning the competing firms in the same industry – is relatively new. Twenty years ago, BlackRock and Vanguard were only very rarely among the top ten shareholders of any firm. On average, common ownership concentration has almost doubled in the last 20 years in the construction, manufacturing, finance, and services sectors.

Our research reveals that common ownership has had a significant impact on the structure of executive compensation. In industries with high common ownership concentration, top executives are rewarded less for the performance of their own firm but rewarded more just for general industry performance.

To understand the effect of common ownership on CEO pay packages, we analyzed total pay (including the value of stock and option grants) of the top five executives of S&P 1500 firms (which cover 90% of U.S. market capitalization) and 500 additional public companies. We studied those pay packages in relation to the firm’s performance, rival firms’ performance, measures of market concentration, and common ownership of the industry. We also examined interactions of profit, concentration, and common ownership variables. This allowed us to estimate both the sensitivity of CEO compensation to the performance of their own firm and of the industry’s other firms, as well the impact that common ownership has on these sensitivities. (We used a variation in ownership caused by a mutual fund trading scandal in 2003 to strengthen a causal interpretation of the link between common ownership concentration and top management incentives.)

We found that when firms in an industry are more commonly owned, top managers receive pay packages that are much less performance-sensitive. In other words, these managers are rewarded less for outperforming their competitors. This difference in compensation has a sizeable effect. In industries with little common ownership, executive pay is about 50% more responsive to changes in their own firm’s shareholder wealth than in industries with high common ownership.

What’s more, in industries with high common ownership, top managers receive almost twice as much pay for the good performance of their competitors as managers do in industries with low common ownership. This effect is even more pronounced for CEOs alone. Essentially, CEOs are rewarded more for the good performance of their competitors than they are for the performance of the company they run.

It’s not just the incentive package. The base pay reflects this, too. Our research shows that top managers’ base pay – the part of pay that does not depend on firm or industry performance – is also higher in industries with high common ownership.

In short, our research suggests that BlackRock, Vanguard, State Street, and other large asset management companies may be endorsing high, performance-insensitive compensation packages, because those don’t encourage competition among portfolio firms. These packages may be inducing managers to carefully consider the impact of their strategic choices on other portfolio firms.

Large asset managers have economic reasons not to incentivize competition among firms they own. After all, their revenue and their investors’ wealth depend on the total value of the portfolios they hold. As a result, it is not in their interest that one portfolio firm competes vigorously against another firm in their portfolio, such as engaging in a price war.

It is not clear that large, diversified shareholders such as BlackRock intentionally choose performance-insensitive CEO compensation for the explicit goal of discouraging intra-industry competition. They may choose it for other reasons, for example, to encourage cooperation or innovation. Maybe it’s not a conscious choice at all. It could simply be that large, diversified investors let performance-insensitive executive compensation slide because their corporate governance efforts are more passive than those of undiversified activist investors.

Still, other empirical studies have identified anti-competitive effects of common ownership. It has resulted in higher prices in the airline and banking industries. The underlying economic rationale is quite simple: if shareholders own not only one, but two or more firms competing in the same industry, these shareholders reap larger gains if the firms they own cooperate rather than compete aggressivelyagainst each other.

A question left open by the previous research is exactly how investors manage to convince the top executives of portfolio firms not to engage in costly price wars against each other, and instead to practice restraint when it comes to competitive strategy. One way to induce managers to act in their investors’ economic interest is executive pay.

But paying executives more when they outperform a competitor (academics call such a reward scheme “relative performance evaluation”) would have the effect of pitting one firm’s CEO against the other and of inducing such costly price wars.

There’s evidence of this dynamic in the tension between smaller undiversified investors (such as hedge funds) and large asset management companies. Whereas the former fight for more performance-sensitive pay that is benchmarked against competitors, the latter vote against them, instead often passing high and performance-insensitive pay that discourages competition between firms in the same industry.

This is an issue – and a tension – that we expect to grow as the trend toward common ownership continues. Our research sheds light on the changing nature of executive compensation, and apparent negative effects of weaker competition and more performance-insensitive pay. However, there may also be positive effects to common ownership. It’s possible that increased cooperation between firms benefits consumers. Certainly, people have benefited from the low-cost, diversified exposure to the stock market that large asset managers offer.

We hope our findings will lead to a better understanding of the effects of common ownership. Shareholders appear to benefit from diversification and higher industry profitability, and there are potential benefits to society from greater cooperation between firms. However, there is a negative impact on consumers due to reduced competition. Ultimately, we hope effective solutions can be reached to resolve the growing tension between shareholders, consumers, and society.

https://hbr.org/2016/10/research-index-funds-are-fueling-out-of-whack-ceo-pay-packages

Bank digital credit risk management

Bank digital credit risk management

To withstand new regulatory pressures, investor expectations, and innovative competitors, banks need to reset their value focus and digitize their credit risk processes.

External and internal pressures are requiring banks to reevaluate the cost efficiency and sustainability of their risk-management models and processes. Some of the pressure comes, directly or indirectly, from regulators; some from investors and new competitors; and some from the banks’ own customers.

The impact is being felt on the bottom line. In 2012, the share of risk and compliance in total banking costs was about 10 percent; in the coming year the cost is expected to rise to around 15 percent. Overall, return on equity in banking globally remains below the cost of capital, due to additional capital requirements, fines, and lagging cost efficiency. All of this puts sustained pressure on risk management, as banks are finding it increasingly difficult to mitigate risk through incremental improvements in risk-management processes.

To expand despite the new pressures, banks need to digitize their credit processes. Lending continues to be a key source of bank revenue across the retail, small and medium-size enterprise (SME), and corporate segments. Digital transformation in credit risk management brings greater transparency to risk profiles. With a firmer grip on risk, banks may expand their business, through more targeted risk-based pricing, faster client service without sacrifice in risk levels, and more effective management of existing portfolios.

Incumbents under pressure

Five fundamental pressures that relate directly to risk management are being exerted on banks’ current business model: customer expectations for digitally managed services; regulatory expectations of a high-performing risk function; the growing importance of strong data management and advanced analytics; new digital attackers disrupting traditional business models; and increasing pressure on costs and returns, especially from financial-technology (fintech) companies (Exhibit 1).

Customer expectations. Traditionally reliant on physical distribution, banks are finding it difficult to meet changing customer needs for speed and simplicity, such as fast online credit approvals.

Regulatory and supervisory road map. Regulators are expecting the risk function to take a more active role in the context of new, digitized business models. New regulations are being put in place to address cyberrisk, automation of controls, and issues relating to risk-data aggregation. Directives pertaining to the Comprehensive Capital Analysis and Review, BCBS 239, and asset-quality reviews specify requirements for data management and the accuracy and timeliness of the data used in stress testing.1

Data management and analytics. Rising customer use of digital-banking services and the increased data this generates create new opportunities and risks. First, banks can integrate new data sources and make them available for risk modeling. This can enhance the visibility of changing risk profiles—from individuals to segments to the bank as a whole. Second, as they collect customers’ personal and financial data, banks are mandated to address privacy concerns and especially protect against security breaches.

Fintech companies and other innovative attackers. The digitally savvy segments have responded to innovative offerings from new nontraditional competitors, especially fintech companies and digital-only banks. These start-ups are extending innovation throughout the digital-banking space, creating a competitive threat to traditional banks but also potentially valuable opportunities for partnerships (Exhibit 2).

Pressure on cost and returns. The new competitors are beginning to threaten incumbents’ revenues and their cost models. Without the traditional burden of banking operations, branch networks, and legacy IT systems, fintech companies can operate at much lower cost-to-income ratios—below 40 percent.

Fighting back

Banks are beginning to respond to these trends, albeit slowly. Over the past several years, leading banks have begun to digitize core processes to increase efficiency—in particular, risk-related processes, where the largest share of banks’ costs are typically concentrated. Most banks started with retail credit processes, where the potential efficiency gains are most significant. Digital approaches can be more easily adopted from well-established online retailers: mobile applications, for example, can be developed to enable the origination of tailored personal loans possible instantaneously at the point of sale. More recently, banks have begun to capture efficiency gains in the SME and commercial-banking segments by digitizing key steps of credit processes, such as the automation of credit decision engines.

The automation of credit processes and the digitization of the key steps in the credit value chain can yield cost savings of up to 50 percent. The benefits of digitizing credit risk go well beyond even these improvements. Digitization can also protect bank revenue, potentially reducing leakage by 5 to 10 percent.

To give an example, by putting in place real-time credit decision making in the front line, banks reduce the risk of losing creditworthy clients to competitors as a result of slow approval processes. Additionally, banks can generate credit leads by integrating into their suite of products new digital offerings from third parties and fintech companies, such as unsecured lending platforms for business. Finally, credit risk costs can be further reduced through the integration of new data sources and the application of advanced-analytics techniques. These improvements generate richer insights for better risk decisions and ensure more effective and forward-looking credit risk monitoring. The use of machine-learning techniques, for example, can help banks improve the predictability of credit early-warning systems by up to 25 percent (Exhibit 3).

Good progress has been made, but it is only a beginning. Many risk-related processes remain beyond the digital capabilities of most banks. Significant effort has been expended on the digital credit risk interface, but the translation of existing credit processes into the online world falls far short of customer expectations for simple digital management of their finances.

There is plenty of room for digital improvement in client-facing processes, but banks also need to go deeper into the credit risk value chain to find opportunities to create value through digitization. The systematic mapping and analysis of the entire credit risk work flow is the best way to begin capturing such opportunities. The key steps—from setting risk appetite and limits to collection and restructuring—can be mapped in detail to reveal digitization opportunities. The potential for revenue improvement, cost reduction, and credit risk mitigation for each step should be weighed against implementation cost to identify high-value areas for digitization (Exhibit 4).

Some improvement opportunities will cut across client segments, while others will be segment specific. In origination, for example, most banks will probably find that several segments benefit from a digitally connected, paperless credit underwriting process (with live access to customer data). At the stage of credit monitoring and early warning, furthermore, advanced analytics and fully leveraged internal and external data could improve risk models for identifying issues across different segments. Back-office and loan-administration tools such as straight-through processing and automated collateral valuation are also cross-cutting improvements, as are the automation and interactivity of risk reporting.

On the other hand, in credit analysis and decision making, banks will likely find that instant credit decisions are mostly relevant in the retail and SME segments, while the corporate and institutional segments would benefit more from smarter work-flow solutions. The application of geospatial data, combined with advanced analytics, for example, can yield a high-performing asset-valuation model for mortgages in the retail segment. For collection and restructuring, automated propensity models will match customers in the retail and SME segments with specific actions, while for the corporate segment banks will likely need to develop debt restructuring-simulation tools, with a digital interface to identify and assess optimal strategies in a more efficient and structured way.

How digital credit creates value

Several leading banks have implemented digital credit initiatives that already created significant value. These are a few compelling cases:

  1. Sales and planning. One financial institution’s journey to an interactive front line involved the construction of a digital workbench for relationship managers (RMs). The challenges to optimal frontline performance were numerous and included the lack of systematic skill building, customer-relationship-management (CRM) systems with a fragmented overview of clients, and difficulty gathering relevant client and industry data. Onboarding, credit, and after-sales processes required many hours of paperwork, drawing frontline attention away from new client meetings. By engaging RMs with the IT solutions providers, the bank’s transformation team created a complete set of frontline tools for a single digital platform, including best-practice CRM approaches and product-specialist availability. The front line soon increased client interactions four to six times while cutting administrative and preparation time in half.
  2. The mortgage process. This presents a large opportunity for capturing digital value. One European bank achieved significant revenue uplift, cost reduction, and risk mitigation by fully automating mortgage-loan decisions. Much higher data quality was obtained through exchange-to-exchange systems and work-flow tools. Manual errors were eliminated as systems were automated and integrated, and top management obtained transparency through real-time data processing, monitoring, and reporting. Decisions were improved and errors of judgment reduced through rule-based decision making, automated valuation of collateral, and machine-learning algorithms. The bank’s automated real-estate valuation model uses publicly known sale prices to derive the amount of real-estate collateral available as a credit risk mitigant. The model, verified and continuously updated with new data, attained the same level of accuracy as a professional appraiser. Recognized by the regulator, it is saving the bank considerable time and expense in making credit decisions on actions ranging from underwriting to capital calculation and allocation. Losses were further minimized by automated monitoring of customers and optimized restructuring solutions. The digital engine moved decision making from 5 percent automated to 70 percent, reducing decision time from days to seconds.
  3. Insights and analysis. By making machine learning a part of the effort to digitize credit risk processes, banks can capture nearer-term gains while building a key capability for the overall transformation. Machine learning can be applied in early-warning systems (EWS), for example. Here it can enable deeper insights to emerge from large, complex data sets, without the fixed limits of standardized statistical analysis. At one financial institution, a machine learning–enhanced EWS enabled automated reporting, portfolio monitoring, and recommendations for potential actions, including an optimal approach for each case in workout and recovery. Debtor finances and recovery approaches are evaluated, while qualitative factors are automatically assessed, based on the incorporation of large volumes of nontraditional (but legally obtained) data. Expert judgment is embedded using advanced-analytics algorithms. In the SME segment, this institution achieved an improvement of 70 to 90 percent in its model’s ability accurately to predict late payments six or more months prior to delinquency.

The approach: Working on two levels

While the potential value in the digital enablement of credit risk management can be significant for early movers, a complete transformation may be required to achieve the bank’s target ambitions. This would involve building new capabilities across the organization and close collaboration among the risk function, operations, and the businesses. Given the complexity of the effort, banks should embark on this journey by prioritizing the areas where digitization can unlock the most value in a reasonable amount of time: significant impact from applying digital levers can be tangible in weeks.

Rather than designing a master plan in advance, banks can in this context develop a digital approach to one area of credit risk management based on existing technology and business value. Each bank may develop initiatives based on their specific priorities. Banks that most need to increase regulatory compliance and the quality of their execution may begin with initiatives in process reengineering to reduce the number of manual processes or to build a fully digital credit risk engine. Those looking to improve customer value from greater speed and efficiency might implement such initiatives as a state-of-the-art digital credit-underwriting interface, a digitally enabled sales force, data-driven pricing, or straight-through credit decision processing. Banks needing to mitigate risk through better decision making may develop initiatives to automate and integrate early-warning and recovery tools and create an automated, flexible risk-reporting mechanism (a “digital-risk cockpit”).

A credit risk transformation thus requires banks to work on two levels. First, look for initiatives that are within easy technological reach and that will also advance the core business priorities. Launching initiatives that bring in savings quickly will help the transformation effort become self-funding over time. Once a first wave of savings is captured, investments can be made in building the digital capabilities and developing the foundation for the overall transformation. Based on what has been learned in early-wave initiatives, moreover, new initiatives can be designed and rolled out in further waves. Typical first-wave initiatives digitize underwriting processes, including frontline decision making and reporting. Risk reporting is another likely candidate for early digitization, since digitization reduces production time and leads to faster decision making.

Building digital capabilities: Talent, IT, data, and culture

The experience of specific initiatives will help shape digital capabilities for the long term. These will be needed to support the overall digital transformation of credit risk management and keep the analytics and technology current. To begin, banks can examine their current capabilities and assess gaps based on the needs of the transformation. The talent focus in risk and across the organization will likely shift as a result toward a greater emphasis on IT expertise and quantitative analytics.

In addition to enhancing their talent profiles, banks will have to shift the direction of their IT architecture. The target will likely be two-speed IT, a model in which the bank’s IT architecture is divided into two segments. Accordingly, the bank’s core (often legacy) IT systems constitute a slower and reliable back end, while a flexible and agile front end faces customers. Without a two-speed capability, the agility needed for digital credit risk management would not be attainable.

Along with the supporting IT architecture and analytics talent, improved data infrastructure is an essential digital capability for the credit risk-management transformation. The uses of data are disparate throughout the bank and will continually change. For big data–analytics projects, great quantities of data are needed, but how they should be structured is not usually apparent at the outset. The construction of separate data sets for each use, furthermore, creates as many data silos within the organization as there are projects.

For these reasons, some leading companies are moving toward utilizing a “data lake”—an enterprise-wide platform that stores all data in the original unstructured form. This approach can improve organizational agility, but it requires that each project has the capability to structure the data and understand data biases. All types of data infrastructure also pose security risks, moreover, which can be addressed only by IT experts. Finally, the reconfiguration of the data infrastructure needs to be done using methods that carefully respect legal privacy barriers and meet all regulatory requirements.

Last, building and maintaining a strong digital-risk culture will be of critical importance in ensuring the success of the risk function of the future. A shift in culture and mind-set is needed among employees, top executives, and regulators, as they acclimate themselves to the new digital credit environment. Here, machines and automation have a much greater role, while human capabilities are developed to support the continual improvement of the risk culture. The focus shifts from executing a risk process to managing true control systems that continuously detect, assess, and mitigate risks.

Toward a flexible digital-risk end state

From data input and management to decision making, from customer contact to execution, the initiatives should build step by step toward a seamless and interactive digital-risk function. The initiative-first approach builds in the capability of agile adaptation to changes in customer demand or the competitive and regulatory environments. The digital opportunities and the way banks address them, in other words, will continually evolve, and the digital end state must support such changes while maintaining enhanced risk-management and client-service capabilities.


The digital transformation of existing credit risk tools, processes, and systems can address rising costs, regulatory complexity, and new customer preferences. The digital enablement of credit risk management means the automation of processes, a better customer experience, sounder decision making, and rapid delivery. Digital-risk management will be the norm in the industry in five years, and banks that act now can attain enduring competitive advantage.

http://www.mckinsey.com/business-functions/risk/our-insights/the-value-in-digitally-transforming-credit-risk-management?cid=other-eml-alt-mip-mck-oth-1608