Community
Risk-based credit pricing can be a great way to expand community-based financial institutions’ loan portfolios while providing the communities they serve with a way to repair credit and obtain needed funding without having to engage with predatory lenders. But it must be done carefully to ensure the financial institution is not unnecessarily exposed to financial risk from losses and is well positioned to assume losses that might occur.
Risk-based pricing strategy
Risk-based pricing is a strategy used to offer different loan interest rates/loan terms to consumers (or businesses) based on numerous factors deemed to represent their ability and willingness to repay the loan (credit worthiness).
Pricing models may consider many factors to determine creditworthiness but generally, they include such elements as:
Credit score/adverse credit history
Employment status
Income
Assets
Collateral
Cosigners, etc.
When creating risk-based pricing models, financial institutions must be careful to adhere to the Equal Credit Opportunity Act which stipulates factors such as race, religion, age, marital status and nationality cannot be used to determine creditworthiness.
Financial institutions must also adhere to the risk-based pricing rule which specifies “that a financial institution that approves a loan or credit card for a borrower with a higher interest rate than what it charges most consumers for the same product must provide the borrower with a risk-based pricing notice. This notice can be delivered by oral, written, or electronic communication.”
Fraud risk and credit risk are inherently linked
In credit risk, underwriting and loan origination are directly impacted by application fraud. What starts out as a fraudulent onboarding resulting from identity fraud—whether a result of first-person, third-person or synthetic Identity fraud—eventually results in loan write-offs for financial institutions.
To put this into dollars and cents, an estimated 10 percent of loan write-offs are due to undetected application fraud.2 That means for a financial institution with $50 million in charge-offs, $5 million could be attributed to fraud. A loss that, if the fraud had been detected during onboarding, could have been greatly reduced or avoided to a large extent.
What is learned both at the point of onboarding and at the time a credit or loan application is submitted (as well as insights gleaned from ongoing transactional and payments fraud monitoring across the customer/member lifecycle) should be incorporated at the customer portfolio level and accessible by both the fraud and credit risk departments.
Fraud risk as part of risk-based pricing
It makes sense then that fraud risk should be a factor in your risk-based pricing strategy, so how can you incorporate fraud risk into your underwriting process?
The answer may already be sitting in your fraud department. If you’re currently using a fraud detection and prevention platform that’s integrated with third-party data intelligence, that allows for a comprehensive orchestration capabilities and is driven by machine learning, you can incorporate fraud risk into your pricing strategy.
Is your risk decision automation platform checking these boxes?
One that does comes integrated with trusted third-party data intelligence providers and can be deployed out of the box with built-in rules or custom rules that can be added, deleted or adjusted based on your institution’s unique risk profile and tolerances. Desired customizations are as easy as drag and drop and don’t require any intervention from IT staff or programmers.
Based on the various external and internal data inputs into the platform and built-in machine learning which keeps refining the system’s ability to profile both fraud and compliance risk accurately, the preferred platform is capable of generating both fraud and compliance metrics and flags that can be incorporated as part of risk-based pricing strategies.
Using a single platform to identify and manage fraud and compliance risk—as well as underwriting risk—allows financial institutions to take a more holistic approach to managing enterprise-wide risk and also realize a greater return on investment from fraud and compliance automation.
This content is provided by an external author without editing by Finextra. It expresses the views and opinions of the author.
Prakash Pattni MD, Financial Services Digital Transformation at IBM Cloud
11 November
Mouloukou Sanoh CEO and Co-Founder at MANSA
Brian Mahlangu VP Product: Digital Platforms Mobile at Absa Bank, CIB.
Roman Eloshvili Founder and CEO at XData Group
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