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How the financial services industry is integrating risk with everything

The financial services sector has been in the process of making risk management more transparent and more accurate since long before the credit crunch.

The first of the Basel Accords, which set a minimum capital requirement for banks (how much money they must hold on to protect against collapse) became law in 1992, and was updated in 2004 with Basel II. The equivalent regulation for the insurance industry, Solvency I, was introduced in 2002.

But the events of 2008 and 2009 changed what was a matter of good corporate citizenship into something far more crucial. Not only did they prove that near-total collapse is possible, they also made financial services regulation a matter of genuine public concern.

Since the credit crunch, updated versions of both the Basel Accords and Solvency have been drafted – Basel III will be enforced from 2019, Solvency II from 2013 – and the banking and insurance industries are currently figuring out how to accommodate the new requirements of this regulation.

Financial services organisations have historically operated risk calculation and reporting as a distinct function of the business, and not without reason. Calculating the risk exposure of a retail bank, for example, requires its own kind of data models, analysis and expertise.

“For many years, risk organisations have operated entirely separately from the marketing and finance divisions,” explains Rick Hawkins, principal advisor at KPMG Performance & Technology. “And each of those separate functions have used separate databases, tailored for their own needs.” However, the information produced by the risk function is invaluable for those other divisions of the business. Likewise, integrating the data collected by the finance and customer departments can improve risk calculations.

This is not a new realisation for the financial services sector, but increased demand for transparency and a requirement to invest capital more effectively are today providing the impetus to put it into action.

The risk of customers

The integration of risk data and analysis with customer and finance operations will be a multi-year, high-investment initiative for the financial services sector, and as such it will test the ability of the sector’s IT departments to cost-justify and implement long-term programmes of change.

According to Edwin van der Ouderaa, global head of Accenture’s financial services analytics practice, though they operate separately, the risk function and marketing department of a financial institution are fundamentally trying to answer the same question. “They are both trying to understand what the different segments among the customers are,” he explains.

In fact, van der Ouderaa says data that has statistical significance for risk calculations often overlaps considerably with the data that is needed to target marketing campaigns or evaluate the right product for a given customer.

“We’ve done some very large customer segmentation analyses for big banks, where we collect up to 800 different variables and find out what the statistically significant clusters of customers are,” he explains. “When we look back at the data, it is usually only a few tens of variables that make a difference in defining those clusters. And you might find that as many as two-thirds of those variables are the same for risk segmentation as they are for marketing segmentation.”

But while both departments analyse what is essentially the same information – who a customer is, what transactions they have conducted, their account history, etc – they define the data in very different ways.

“They might not even share a definition of what a customer is,” he says. “For example, the customer for the risk department might be the breadwinner in a household, whereas for the marketing department it might be the entire family.”

This separation of data models has in the past had some absurd consequences, says Tony Brown, senior finance industry consultant at data warehouse vendor Teradata and a former marketing executive in the banking sector.

“I’ve seen cases where the marketing department would identify thousands of customers to be offered a new product,” he says, “but when those customers accepted the offer, as many as half of them would be rejected by the risk department.”

As well as preventing wasted effort such as this, integrating risk and marketing data allows for risk-based pricing, i.e. pricing a financial product such as a loan based on the risk that the customer will default.

This is by no means a new concept – it is standard practice for large, institutional loans, and it is practically the basis of the insurance industry. However, the desire among banks to invest their capital more efficiently means that their retail functions are making greater use of risk-based pricing.

There has been some public opposition to risk-based pricing (in fact, it is illegal in the US for certain products) and some banks have made a virtue of not using it. But according to Brown’s colleague James Hunt, “the long-term trend is against that, realistically. Risk-based pricing is getting more common and more sophisticated.”

Beyond risk-based pricing lies risk-based portfolio management, explains KMPG’s Hawkins. “There has been a view that greater profitability comes from winning a greater share of a customer’s wallet – that’s been a driver for the likes of Barclays as they have realised that industry consolidation is starting to squeeze them,” he says.

“But offering more products to a customer may actually expose a bank to greater risk. Integrating the risk and customer data allows them to make decisions about whether taking a greater share of your wallet is a good or a bad risk.”

Hawkins reports that some banks see the integration of customer and risk data as a source of competitive advantage. “Santander is probably the leaders in this,” Hawkins explains. “If you phone a Santander call centre, the agents will probably have some idea of what kind of risk profile you have.”

Meanwhile, more conservative banks see it as a way to minimise their risk exposure. “Knowing the risk profile of a customer when they call in allows them to know how interacting with that customer will affect their overall position,” he explains.

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