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Lending to SME's comes with some unique challenges: The credit decision must account for the 2 sources of credit risk: 1) The Individual Owner(s) 2) The Business.
Lenders must decide whether to lend, how much and at what price by balancing risk vs reward.
Credit Risk models that account for the dynamic & complex nature of the source of risk in SME in combination with balancing risk vs reward for pricing & limits.
Decision Science enables more credit decisions to be automated so customers can be instantly approved.
Confident data driven decisions enables you to automate decisions creating acquisition efficiencies in underwriting & account management
The drivers of the credit decisions for SME lending come from 2 key sources
Balancing the trade off between risk & reward is key to any SME lending credit decision however both sides of the equation have some complexities that need to be managed to make optimal credit decisions.
Managing Credit Risk:
One distinction between individual lending and SME lending is the diversity in source of information. This may be as simple as one person (owner) and one business entity). As the scale increases, there may be a complex network of owners, guarantors, directors & their related businesses.
Addressing these issues from diversity of relationships to sources of information value for modelling & decisioning requires best in class decision science to enable optimal credit decisions.
Increasing Revenue:
SME Lenders have a number of credit policy leavers to pull to enable them to win vs the competition depending on the type of SME lending product, however the challenge is understanding how changing credit policy will impact the future cash flows of the lender.
Most SME lenders use legacy segmentation grid based techniques for setting prices & limits
SME Lenders can make better & faster credit decisions by using decision science to optimize each customer interaction.
In the case of multiple owners, you would want access to all of their credit bureau data; in the case of one business, you would want business bureau data. Lastly, in the case of relationships between other businesses and this business or between owners of this business and their ownership interests in other businesses, you would want to capture those facts and possibly extended credit history.
When it comes to making the best approve vs decline decision & optimal price & limit for maximizing future cash flows whilst minimizing credit losses decision science can be used to compute expected profit for each customer in real time and deliver the optimal decision back to the customer.
We are experts in SME lending decisions and understand how to handle the credit risk variation by scale challenges of SME credit risk modelling.
Set personalized & optimized prices and limits to win more of the right customers
Compute optimal decisions in real time and easily integrate them with your existing
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