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Risk-Based Pricing and Beyond

Daniele Vergari
Carlo Gabardo
Senior Business Consultants
Experian Decision Analytics Global Consulting Practice

Value-based risk selection strategies: from nice-to-have to must

The latest version of the international capital adequacy regulation (Basel III) with its increased capital requirements and the rising competition on the global financial markets are putting margins in the financial sector under serious pressure, making the implementation of value-based risk selection strategies a must to all the market players rather than the “nice-to-have” they used to be in the past.

Although many financial institutions have already leveraged Basel models and tools that have become more and more sophisticated (moving from decisions based on probability of default to decisions based on expected loss, which includes the severity of the loss given the default), there is still room for improvement in current practices. In general, optimal decision making is not only a matter of default risk, but it is also related to the potential value or profit that each application can generate. Such profit depends on several elements, of which expected losses is only one.

The table below shows the basic Profits & Loss statement of any lending decision. The risk component (or the unexpected loss linked to a credit decision) is given by the cost of the capital at risk or economic capital (as derived using an internal VaR model or using the Basel IRB formulas as a proxy). Including the cost of risk is essential for a more holistic view of any lending decision. Neglecting it causes an underestimation of the total cost.

Costs

Revenues

Cost of funding

Interest income

Operational costs

Other revenues

Expected loss

 

Cost of economic capital

 

Table 1 – Example P&L

Risk-based pricing and market price

It is common sense that riskier clients should be charged a higher price than less risky ones. However, selection or pricing strategies based on risk adjusted customer value are not as common practice as one might expect. In several markets, evidence shows that there is no link between price and risk, as shown in the figure below (the current price is the observed price for a retail credit portfolio). On the contrary, the risk adjusted price curve shows the minimum (theoretical) price that should be applied to each applicant in order to match all costs, cost of risk included: the higher the risk, the higher the price.

In many markets, though, several institutions act as price taker (adopting market prices) and adversely select some of their clients. This means that they accept some clients that they should have rejected as they would be unprofitable (area B in the figure below). As a consequence, a part of the portfolio is not generating value, maybe still leading to positive, but suboptimal, portfolio performance (as area A may be greater than area B).

Figure 1 – Market price vs. risk-based pricing

In fact, many financial institutions are unable or reluctant to use a full risk-based pricing strategy. However, they could still improve their current risk management practice. More advanced decision making processes based on risk-adjusted assessment of the application (or client) can still be applied, by:

  • Deciding on a credit application on the basis of the expected risk-adjusted performance, using any risk-adjusted performance measure, such as RORAC;
  • Varying the terms of business in order to reduce the costs and match revenues, leaving the price unchanged.

Risk-adjusted-performance-based decisions: the RORAC selection approach

In the first case, the expected risk-adjusted return of any application is calculated taking into account the P&L statement shown above, the price being given (whereas in the risk-based pricing strategy, the price is unknown at this point). The application is accepted if expected return is positive or equal to zero (this means the application is expected to generate revenues greater or equal to the estimated costs). Under such an approach, the application is only accepted if it creates value with the current terms of business.

Risk-based processing

Under the RORAC selection approach, applications with a negative ratio have to be rejected as unprofitable. However, financial institutions can alternatively decide to change contract conditions (e.g., by asking for collateral) in order to reduce the costs (e.g., expected and unexpected losses) and then balance them with the revenues generated by applying the current price. This is the risk-based processing approach.

This process is graphically detailed in Figure 2. The case of a RORAC < 0 is in the area where the current (or market price) is lower than the risk-adjusted one (the minimum price). The contract terms do not grant adequate coverage for all costs in their current state. New conditions are set to reduce the costs, pushing the minimum acceptable (risk-adjusted or theoretical) price back to the one currently applied.

Figure 2 – Risk-based processing strategy

The choice of conditions to adjust in order to achieve the ‘right’ risk-profit relation is fundamental because they usually have a very different impact on the cost of risk. The table below provides the typical risk drivers and mitigants and their effectiveness on risk correction.

Risk driver

Effectiveness

Collateral

High

Down payment/Deposit

Medium

Loan amount

Low

Term

Low

Table 2 – Impact of risk drivers on cost

Risk-based processing proves to be more effective when collateral can be included in the contract conditions. Reducing the loan amount (eventually together with the term) would leave some credit needs unsatisfied, thus forcing the client to meet them through other financial institutions in the market place. As a consequence, the client global risk profile would be somewhat underestimated. Some may argue that when the applicant receives a counter offer for a lower amount, for instance, in the case of personal loans, he/she will actually reduce or limit his/her needs. Psychologically, the applicant may be willing to change his/her plans. However, that may not work when there is ‘real’ need for finance, like financing primary needs.

Conclusions

Managing risk more actively is now becoming key to succeed in more regulated and more competitive markets, and the three strategies (risk-based pricing, risk-based or RORAC selection and risk-based processing) are being used for a variety of purposes: some financial institutions are facing the problem of managing new market niches, such as sub prime clients; some others are reviewing the strategies (combination of price, term, down payment, etc.) for applications generated through the dealer network; some are simply facing the need to increase sales and profit.

All of the three approaches aim at maximising the expected risk-adjusted portfolio profitability however, under normal operating conditions just few constraints can be easily considered, such as the rejection rate. In fact, although profit maximisation can be a very appealing target, it can have unexpected effects on financial institutions’ other key performance metrics, like expected portfolio bad rate, expected losses, provisions, minimum capital requirements etc. Therefore, these strategies should be applied in a flexible way, balancing appropriately the different strategic targets. For instance, blindly applying a risk-based strategy to all applicants could have the unwanted effect of increasing adverse selection rather than eliminating or reducing it. If no entry barriers are set, high risk clients that face difficulties in obtaining credit may be willing to pay more in order to get it. The financial institution applying the risk-based pricing strategy will end up attracting such customers, with obvious impacts in terms of the expected quality of the portfolio.

More realistically, the right approach can be based on a combination of different strategies, for example, to keep selecting applicants depending on their probability of default, and then adjusting the price in all or part of the accepts. Moreover, more complex strategies which consider the various objectives and constraints that have to be met can be adopted. In practice, an institution willing to achieve the maximum benefits (e.g., portfolio profit) given a set of constrains (e.g., max level of expected losses, max amount of economic capital, etc.) has to face much more complex problems, of which price selection is just one element. This is particularly true in the new scenarios produced by the new Basel III regulation which sets new liquidity constraints and much tighter minimum capital standards. In these scenarios banks have to preserve their profitability, already threatened by financial instability and global competition, meeting the tighter regulatory constraints. Under these circumstances, strategy optimisation is then the right solution because it allows the optimisation of a goal function (e.g. risk-adjusted profit) under a number of constraints. These optimisation techniques can in fact be used to optimise the decision process selecting optimal conditions (i.e. pricing, loan amount, capital allocation, etc.) which maximise the expected profit, meeting business and regulatory constraints.

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