Improving SME Credit Risk Management with Advanced Predictive Analytics

One of the key issues in risk management for micro, small and medium firms (SME) is the scarcity of reliable data for credit risk evaluations1. A typical loan evaluation includes an in-depth financial profiling, followed by a free-text recommendation by a specialized analyst. A second analyst complements this report with all available socioeconomic and behavioural data, and then decides whether the entrepreneur is credit-worthy. This process is cumbersome, inefficient, and bias-prone, resulting in underfunding for small businesses2, and subsequent economic inefficiencies. The research will address the following questions:
1. What is the most efficient design of a deep neural network that can process the diverse data created during SME evaluations?
2. Can this automated model reach better efficiencies in statistical accuracy, monetary cost, bias reduction, and evaluation time over simulated conditions, and in real data?
3. What would be the impact of this new method in regulatory and financial terms?