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Data & Analytics

10:27 AM
Nigel Smith and John McHugh, Accenture
Nigel Smith and John McHugh, Accenture

Use Analytics to Peer into the Future

Predictive analytics can produce massive amounts of insight -- leading to greater demand for more and quicker decisions. Are banks ready for that challenge?

Banks that have emerged strongest from the financial crisis benefited, in part, from their astute use of real-time predictive analytics. These firms had the systems and organizational structures to capture and act upon early warning signals, monitor a changing environment and adjust responses accordingly. They not only understood what had happened in the past, but what it meant, and what they needed to do next.

In the face of increased regulation and new capital requirements, those institutions that prosper will have the analytic horsepower to understand which customers and products deliver the best risk-adjusted return and to master their liquidity positions and risk exposures. Banks seem to be getting this message. In a recent Accenture survey of 600 U.S. and U.K. executives, two-thirds said their top long-term priority in analytics is to increase their ability to model and predict customer behavior and decisions.

But there is still a long way to go -- a substantial minority said that their current technology greatly hinders the effective use of enterprise-wide analytics. Indeed, many banks remain burdened with a legacy approach to analytics. They need to address:

  • Siloed business lines using different and incompatible analytical technologies;
  • Inconsistent data with different definitions, taxonomy and levels of granularity and refresh rates; and
  • An over-reliance on descriptive, rather than predictive analytics.
  • Descriptive analytics identifies historical problems, how, where and under what circumstances they occurred, and can even point to action that might resolve them. Good, but not good enough in today's reconfigured financial world.

    Predictive analytics, by contrast, answers the "so what" question. It requires the outputs of descriptive analytics but goes far beyond them, adding an additional layer of insight by synthesizing and supersizing those outputs for action. Sophisticated algorithms and statistics are applied to manipulate the data into management tools that can help executives make more informed, fact-based decisions that deliver better business outcomes.

    Unlocking Value

    Most of a bank's potential profitable growth is locked within mass-market segments that represent 90 percent of the customer base. Especially now, with capital and regulatory constraints demanding optimum performance, banks can unlock a massive opportunity in that segment.

    Analytics can help banks boost revenues, for example:

  • One institution achieved 12 percent incremental revenue generation with no additional marketing expenditure.
  • Another lowered marketing costs by 25 percent while increasing revenue by 4 percent.
  • Another bank improved cross-selling by almost 40 percent.
  • In Italy, two leading banks have developed loan and management programs based on analytics, and a portfolio management analytics portal to allow client default monitoring. The result: a 3 to 4 percent reduction in defaults and 100 to 200 basis point reduction in foreclosure rates.

    A major U.S. insurance company has achieved competitive advantage by building its corporate culture around analytics. Mining widely available insurance industry data, for example, it defined narrow groups or cells of customers, used regression analysis to identify the factors most commonly correlated with losses engendered by each cell, and then set prices for the cell that would enable it to earn a profit across a portfolio of customer groups as confirmed by simulation software which tested the implications of its hypotheses.

    Be Ready for New World

    Forward-looking analytics can produce massive amounts of insight which, in turn, leads to greater demand for more and quicker decisions. So the big question then becomes: Are banks ready to tackle that challenge?

    If, for example, a bank's new technology suddenly enables it to produce insights on credit risk every hour instead of every three days, the bank must be able to manage that level of complexity and sophistication. Similarly, will the risk committee's traditional weekly meeting be sufficient once risk data is available and analyzed in a fraction of the time that it was previously?

    Clearly, new analytic capabilities will transform the decision-making process. For banks, that means making sure that their staffing, training, governance, systems and controls are adequate so that they are ready to use the new insights effectively and not get overwhelmed

    In this post-crisis environment, every financial services company should be looking to ramp up their analytical capabilities. Not having the right analytics in place to leverage and direct data appropriately, and the organization to effectively manage the insights produced by the data, can severely undermine a bank's ability to outperform its peers.

    Nigel Smith is Managing Director, Management Consulting, in Accenture's financial services practice; John McHugh is a senior executive in Accenture's financial services practice.

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