Risk analytics enters its prime

Here is a brief excerpt from an article written by Rajdeep Dash, Andreas Kremer, Luis Nario, and Derek Waldron for the McKinsey Quarterly, published by McKinsey & Company. To read the complete article, check out other resources, learn more about the firm, obtain subscription information, and register to receive email alerts, please click here.

To learn more about the McKinsey Quarterly, please click here.

*     *     *

All the ingredients are in place for unprecedented advances in risk analytics. Now it’s up to banks to capture the opportunities.
With the rise of computing power and new analytical techniques, banks can now extract deeper and more valuable insights from their ever-growing mountains of data. And they can do it quickly, as many key processes are now automated (and many more soon will be). For risk departments, which have been using data analytics for decades, these trends present unique opportunities to better identify, measure, and mitigate risk. Critically, they can leverage their vast expertise in data and analytics to help leaders shape the strategic agenda of the bank.Banks that are leading the analytical charge are exploiting both internal and external data. Within their walls, these banks are integrating more of their data, such as transactional and behavioral data from multiple sources, recognizing their high value. They are also looking externally, where they routinely go beyond conventional structured information, such as credit-bureau reports and market information, to evaluate risks. They query unconventional sources of data (such as government statistics, customer data from utilities and supermarket loyalty cards, and geospatial data) and even new unstructured sources (such as chat and voice transcripts, customer rating websites, and social media).
Furthermore, they are getting strong results by combining internal and external data sets in unique ways, such as by overlaying externally sourced map data on the bank’s transaction information to create a map of product usage by geography. Perhaps surprisingly, some banks in emerging markets are pioneering this work. This is possible because these banks are often building their risk database from scratch and sometimes have more regulatory latitude.The recent dramatic increases in computing power have allowed banks to deploy advanced analytical techniques at an industrial scale. Machine-learning techniques, such as deep learning, random forest, and XGBoost, are now common at top risk-analytics departments. The new tools radically improve banks’ decision models. And techniques such as natural-language processing and geospatial analysis expand the database from which banks can derive insights.These advances have allowed banks to automate more steps within currently manual processes—such as data capture and cleaning. With automation, straight-through processing of most transactions becomes possible, as well as the creation of reports in near real time. This means that risk teams can increasingly measure and mitigate risk more accurately and faster.

The benefits—and challenges—of risk analytics

Banks that are fully exploiting these shifts are experiencing a “golden age” of risk analytics, capturing benefits in the accuracy and reach of their credit-risk models and in entirely new business models. They are seeing radical improvement in their credit-risk models, resulting in higher profitability. For example, Gini coefficients of 0.75 or more in default prediction models are now possible.1 Exhibit 1 lays out the value that analytics can bring to these models.

Some banks are expanding their risk models to new realms. A few have been able to automate the lending process end-to-end for their retail and SME segments. These banks have added new analytical tools to credit processes, including calculators for affordability or preapproval limits. With this kind of straight-through processing banks can approve up to 90 percent of consumer loans in seconds, generating efficiencies of 50 percent and revenue increases of 5 to 10 percent. Recognizing the value in fast and accurate decisions, some banks are experimenting with using risk models in other areas as well. For example, one European bank overlaid its risk models on its marketing models to obtain a risk-profitability view of each customer. The bank thereby improved the return on prospecting for new revenue sources (and on current customers, too).

A few financial institutions at the leading edge are using risk analytics to fundamentally rethink their business model, expanding their portfolio and creating new ways of serving their customers. Santander UK and Scotiabank have each teamed up with Kabbage, which, using its own partnership with Celtic Bank, has enabled these banks to provide automated underwriting of small-business loans in the United Kingdom, Canada, and Mexico, using cleaner and broader data sets. Another leading bank has used its mortgage-risk model to provide a platform for real estate agents and others providing home-buying services.

Realizing the potential

For many banks the advantages of risk analytics remain but a promise. They see out-of-date technology, data that is difficult to clean, skill gaps, organizational problems, and unrelenting regulatory demands. The barriers seem insurmountable. Yet banks can get things moving with some deliberate actions (Exhibit 2).

Perhaps the most salient issue is that risk analytics is not yet on the strategic agenda. Bank leaders often don’t understand what is really at stake with risk analytics—at times because the analytics managers present highly complex solutions with a business case attached as an afterthought. Lagging banks miss out on the benefits, obviously, and also put other programs and activities at risk. Initiatives to grow revenue and optimize pricing can founder if imprecise risk assessment of customer segments leads to poor choices. In lending, when risk models underperform, banks often add business rules and policies as well as other manual interventions. But that inevitably degrades the customer experience, and it creates an opening for fintechs to capture market share through a better experience and more precise targeting. Taken to its logical conclusion, it is conceivable that banks might be relegated to “dumb pipes” that provide only financing.

Some nimble risk groups are finding ways through these problems, however. Our analysis suggests these teams have six common behaviors:

  • Take it from the top, lifting risk analytics to the strategic agenda. For example, 4 out of 10 strategic actions that HSBC Bank laid out in 2015 rely heavily on risk analytics.
  • Think big and apply analytics to every material decision. Capital One is well-known for applying analytics to every decision that it makes, even when hiring data scientists.
  • Go with what you have. If data is messy or incomplete, don’t wait for a better version or for a “data-lake nirvana.” Use the data you have, or find a way to complement it. When Banco Bilbao Vizcaya Argentaria (BBVA) wanted to lend to some clients but lacked information, it partnered with Destacame, a utility-data start-up, to provide data sufficient to support a way to underwrite the customers.
  • Accumulate skills quickly, through either rapid hiring or acquisitions and partnerships. Then retain your talent by motivating people with financial and nonfinancial incentives, such as compelling projects. Banks such as BBVA, HSBC, Santander, and Sberbank have launched funds of $100 million and more to acquire and partner with fintechs to add their market share, sophisticated technologies, and people.
  • Fail often to succeed, iterating quickly through a series of “minimum viable products” (MVPs) while also breaking down traditional organizational silos. One bank building a fully digital lending product went through six MVPs in just 16 weeks to get to a product it could roll out more broadly.
  • Use model validation to drive relentless improvement. Validation teams can be the source of many improvements to risk models, while preserving their independence. The key is for teams to style themselves as the guardian of model performance, rather than the traditional activity of merely examining models.

If banks can master these elements, significant impact awaits. Risk analytics is not the entire answer. But as leading banks are discovering, it is worthwhile in itself, and it is also at the heart of many successful transformations, such as digital risk and the digitization of key processes such as credit underwriting.

Risk-analytics leaders are creating analytic algorithms to support rapid and more accurate decision making to power risk transformations throughout the bank. The results have been impressive. An improvement in the Gini coefficient of one percentage point in a default prediction model can save a typical bank $10 million annually for every $1 billion in underwritten loans.2 Accurate data capture and well-calibrated models have helped a global bank reduce risk-weighted assets by about $100 billion, leading to the release of billions in capital reserves that could be redeployed in the bank’s growth businesses.

*     *     *

Here is a direct link to the complete article.

Rajdeep Dash is a senior expert in McKinsey’s London office, Andreas Kremer is a partner in the Berlin office, and Luis Nario and Derek Waldron are partners in the New York office.


Posted in

Leave a Comment

This site uses Akismet to reduce spam. Learn how your comment data is processed.