Maximizing the efficacy of risk-management toolkits

The role of any risk assessment team is, in many ways, to predict the future outcome of a potential borrower by making judgement decisions based on information currently available. Accomplishing this requires consideration of a wide variety of factors and adaptations to real-time information changes.

Assessing the risk of a loan is an essential step for any lender. That said, the process itself can be time-consuming, labor-intensive and prone to make error, particularly if done manually.

To overcome those obstacles, a quality loan origination software (LOS) solution will provide support in refining risk management procedures.

Wiser decisions with alternative data

Traditionally, lenders determine their credit policy based on pre-bureau rules and post-bureau rules. The credit policy is paramount in allowing a system to make quick decisions on declining an application or flagging it for review. However, a risk analyst ideally has access to a broad dataset and can essentially look at different data from application or credit reports to implement or change rules.

Credit bureaus have already started looking at alternative data points and how these can be introduced in credit scoring to allow more people access to credit lines for which they would otherwise not qualify. Since 2017, all three major bureaus have acquired alternative data.

Lenders can also expand their data on a potential borrower by purchasing alternative data sources from third-party vendors that are not directly related to credit bureaus. This data can include utility payments, cell phone bills and other financial information that can back up the borrower’s creditworthiness.

Alternative data on its own, however, won’t make life any easier. It’s imperative that a lender’s LOS provider either integrates with preferred alternative data sources or allows access to them for use in decision making.

Scorecards: measuring potential

It’s common practice for lenders to use a risk scorecard when examining a potential borrower’s likelihood of repayment, with most lenders making use of custom scorecards designed around their specific requirements.

We’d recommend that if lenders don’t currently have their own scorecards, they should strongly consider designing them in-house to give them complete control over risk, while still allowing for manipulation and changes at will.

When designing custom scorecards, there is the option to develop individual attributes. However, this is becoming less common as the risk and maintenance costs associated with attributes development is very high; this work must also be fully compliant and consistent across all main credit bureaus.

The recommended option is to purchase third-party attribute sets via credit bureaus such as Experian ATB and Equifax ADA. Doing so gives lenders access to comprehensive attribute sets without requiring them or their team to do the manual coding. It also significantly mitigates risk, as attribute data sets are already standardized across all three credit bureaus and the burden of compliance falls on the provider.

Another consideration where it comes to scorecards is the scenario in which a lender has just one set of scorecards, necessitating significant changes for a transition to a new generation and increasing the corresponding risk.

One way these associated risks can be mitigated is by gradually introducing the new generation scorecard into their operation. This is, of course, dependent on the LOS provider’s ability to run a champion-challenger model.

Pricing

Analytics and statistical models are used to pinpoint the most accurate pricing strategy for any given loan. The unfortunate downside of these statistical models is that they become bogged down and challenging to adapt to new borrowers as more lending data is collected and analyzed.

Conversely, by using a fully-featured, risk-based pricing engine integrated within their LOS, lenders can manipulate a range of criteria to respond to current market conditions. Ideally, the LOS would facilitate a risk management approach that can be quickly adapted to live statistics. Risk analysts can then easily set custom base rates, adjusters, cutbacks and workflows at the flick of a switch, focusing on evaluating the results of pricing automation rather than the process.

A good lender will aim to use any variable available to assist in loan pricing; anything less makes for a pricing model too dependent on a traditional concept of credit history and subject to the limitations of manual assessment. Furthermore, a flexible LOS and easily-adjustable pricing mechanisms and processes are ideal for the risk team to manage real-time pricing changes.

Pricing — consumer loans

In consumer lending, the maximum loan amount is most often determined by secured collateral. This type of pricing can be intricate and time-consuming due to the number of variables involved in determining a borrower’s risk of default.

Lenders can also offer ancillary products, such as warranties and insurance, as part of their loan package. Including these add-on variables brings another layer of complexity to the pricing process.

Pricing — auto loans

As auto lending is primarily indirect and goes through an intermediary — an auto dealership — it makes pricing even more important. Using risk-based pricing for auto loans means that lenders can factor in down payment, rate, make and model of the vehicle, fees and the inclusion of ancillary products into the equation. The relationship between these variables should be fully automated and configurable based on rules provided by the risk team.

Lenders should be able to automatically prepare and present a dealership with several different loan options from their LOS, thus providing the dealership with multiple ways to entice a potential borrower. This type of offering involves weighing pricing variables against one another, along with a thorough assessment of the borrower. However, this approach provides the risk team valuable data which can be analyzed and fed back into the business rules.

Pricing — subprime lenders

For subprime lenders, or even lenders who want to dabble in subprime, the process of introducing risk-based pricing can become burdensome. In addition to the criteria used in consumer and auto lending, they must factor in additional variables such as business logic, state of origination, returning customer status and promotional status.

The LOS should allow lenders to introduce all these business rules, as well as change them frequently, without excessive additional overheads or costs if their current model doesn’t work well for their operation.

Champion-challenger model

The “champion” model operates as standard, while the “challenger” runs in the background, not affecting any data in real time.

For example, a lender may be considering using alternative data points to assess the creditworthiness of potential borrowers but is unsure how valid this new data will be in their risk assessment. A champion-challenger model allows them to run their regular risk assessment for the borrower, while at the same time simulating an evaluation that includes the new or alternative data points in the background.

Once enough data is collected from the simulations, a decision can be made on whether the challenger is a better solution; it can then be integrated into the LOS in place of the previous champion.

The advantage of running multiple risk assessment and pricing models in parallel is that lenders can dramatically decrease their risks, whilst also increasing their decision space to quickly move towards an optimal approach.

Improving efficiency and reducing costs

Risk analytics is entering its prime via the use of innovative technological tools and advancement, according to consulting firm McKinsey & Company.

Integrating automation into risk management can ultimately streamline the details of the process, allowing risk teams to focus their attention on the analysis of results rather than the steps to obtaining them.

In short, risk analytics creates an opportunity for lenders to reduce costs and increase the efficiency of their risk team, which is a win-win for any lending business.

Are you relying on a crystal globe, or is your toolkit properly equipped to divine what the risk of a loan will be?

Shim Mannan is the executive vice president of product and business development in the Americas for IDS (formerly White Clarke Group)