Martin Fleming, IBM’s chief analytics officer and chief economist and the keynote speaker for the Smarter Risk Summit in London, scheduled for 8 November 2016, outlines the challenges and opportunities that central bankers face as they attempt to satisfy regulatory requirements in a distressed global economic climate. Learn more about his recommendations for how financial institutions can adapt to the complex realities ahead, whether by investing in cognitive technology or by building a team trained in managing data, process and policy.
1. You’ve characterized the current global economy as being in a state of “low-level equilibrium.” Can you expand on that? What does such an environment mean for banks?
The global economy is indeed “stuck in a low-level equilibrium,” with real growth below its long-term average for the 11th year in a row, inflation below its long-term average for the 22nd year in a row and productivity growth nearly non-existent. What’s more, we see such dismal economic performance even though interest rates are at extraordinarily low levels—negative in some cases—and despite the easiest monetary policy of any era other than periods of hyperinflation. The “low-level equilibrium” we see is caused by the conjunction of a number of factors, including three and a half decades of diminished demand for private- and public-sector fixed investment spending following three decades of massive infrastructure build-up incorporating technology from the 1940s to 1960s.
Diminished demand results from the depreciation that is a natural part of the notably long life cycle of capital infrastructure. The long, slow period of physical depreciation has slowed productivity growth and caused wages to stagnate. The result has been a substitution of labor for capital. As return to capital has declined in recent decades, the financial services sector—as is its role—has created new asset types. These additional assets have been attractive to investors, but—as we’ve seen—they have ultimately proved ephemeral. The balance sheet adjustments required after the bubble period have depressed aggregate demand.
After the Great Recession and financial crisis of 2008–2009, the United States had by 2012 completed an adjustment that now nears completion in the eurozone and the Chinese and Japanese economies as well. Accordingly, advanced market economies’ lack of desire to engage in discretionary fiscal policy has slowed growth and shrunk government sectors.
For banks, the current global macroeconomic environment means that monetary policy will remain “easy” for some time to come, keeping interest rates low—even negative in some national economies. Likewise, with business investment spending growth remaining weak, liquidity will remain abundant, and lending opportunities will be diminished by historical standards.
Furthermore, as a result of the Great Recession and financial crisis and the diminished aggregate demand that has ensued, the banking sector has shrunk. In the United States, employment in depository institutions sits 8 percent below its peak of September 2008. Clearly, low and negative interest rates have reduced bank profitability.
2. What challenges do banks face as they attempt to satisfy regulatory requirements?
Beyond creating obvious issues of cost and expense, heightened regulatory requirements are intended to reduce risk across the banking system. Yet risk reduction inherently means reduced return to investors. With interest rates at or near historic lows and equity markets stagnating, investors are clamoring for higher returns. In such an environment, banks risk losing market share to less tightly regulated nondepository institutions, such as hedge funds and other investment managers.
Furthermore, leaders in the banking sector must expect more competition from outside their industry. New market entrants, such as startups in financial technology—fintechs—are intensifying competition, yet they also present partnering opportunities.
3. What role does cognitive computing play in overcoming those challenges?
To thrive in such an environment—in which regulatory requirements have brought about a massive expansion of available data, in which returns have collapsed and in which new competitors have emerged—banking and financial services leaders must be smarter about how they manage and govern data. They should begin by honing their abilities to engage with customers, discover deeply hidden insights and make decisions.
Heightened levels of engagement will help enhance communication and collaboration, thus making possible increasingly tailored and effective services. New discovery tools and capabilities can unearth the insights that are buried in data, facilitating the development of innovative products and services. Ever more accurate and timely decision capabilities can lead to increasingly personalized recommendations for customers while enhancing decisions relating to risk, security and fraud detection.
Cognitive computing builds on data governance and analytics capabilities by applying machine learning algorithms and natural language processing to make sense of vast quantities of largely unstructured data, thereby creating additional opportunities for data-driven discovery and decision making. Although financial institutions can still derive value from analytics solutions, they can reach previously unattainable levels of value through the addition of cognitive capabilities.
4. As paradigm shifts go, reforming a legacy risk framework is monumental. What are the steps needed for successful implementation?
Increased regulatory requirements have spurred the creation of vast stores of data and information. But these new information assets can do more than merely help financial institutions comply with legally mandated requirements, for they can be a source of significant business value. Advances in cognitive computing can help financial institutions manage an ever-increasing volume of data while exploiting it for ongoing insights.
Cognitive computing is a powerful evolution in digital banking, and it opens the door for banks to leverage their wealth of data in ways not possible for new market entrants such as fintechs. Cognitive capabilities help banks extract meaningful patterns from data about markets, customers, partners and employees, then use that information to anticipate change—even to shape the future.
5. Tell us how the IBM Watson platform can address macroprudential risk.
IBM’s Watson platform delivers cognitive capabilities to the banking sector. Cognitive approaches fundamentally change the ways in which humans and systems interact, significantly extending human capabilities by leveraging humans’ ability to provide expert assistance. This approach can thus help provide advice by developing deep domain insights and bringing information to light in a timely, natural and usable way. Cognitive systems augment the experience and judgment of bankers by consuming vast amounts of structured and unstructured information, reconciling ambiguous and even self-contradictory data and learning from regulatory outcomes. In so doing, they act as advisors by suggesting a set of options to the human users who will make the final decisions.
What’s more, cognitive systems can help users discover insights that might not otherwise come to light, identifying insights and connections while making sense of the vast amounts of information available around the world. Cognitive systems thereby aim to aid decision making, alleviating human bias by offering evidence-based options. Accordingly, they continually evolve based on new information, results and actions.
6. In facing the complex realities ahead, from understanding the enormous volume of data amassed by banks to communicating the urgency of risk reform to the entire organization, what advice do you offer to the stakeholders involved in this process?
Banking leaders must realize that a steep learning curve is often involved in designing and scaling cognitive systems. Viewed from the perspectives of implementation and user interaction, cognitive systems are fundamentally different from traditional programmatic systems. Accordingly, banking and financial services organizations can follow three particular recommendations as they learn from pioneering organizations that have already implemented cognitive capabilities.
First, define value by finding the right opportunity. Cognitive solutions are well suited to a defined set of challenges. Banking and financial services organizations should analyze specific problems to determine whether cognitive capabilities are indeed appropriate.
Second, prepare the foundation by investing in human talent; building and maintaining a high-quality corpus of data and information; and considering policy, process requirements and impacts. Cognitive solutions are “trained,” not programmed; they “learn” through interactions, from results and from new pieces of information. Such solutions help organizations scale expertise, but they are only as good as their data. Accordingly, invest ample time in selecting the data to be included in the corpus—both structured and unstructured. Assess any potential impact on processes and on how people work.
Third, manage change. Ensure executive involvement, which should begin with active participation in defining the cognitive vision and roadmap. Communicate the cognitive vision at every level. Address all fears, uncertainties and doubts head-on, making sure to leverage executive sponsors to reinforce the value of a cognitive approach to your institution’s mission. Education is a fundamental part of ensuring that a cognitive approach is understood and adopted.
To learn more about how a cognitive approach can empower your institution, register for the Smarter Risk Summit 2016. While you’re preparing for the conference, connect with IBM Risk Analytics professionals to start your cognitive journey today.