Credit

Credit Lending

Credit lending has traditionally been driven by mathematical credit rules used to profile customers based on pre-selected criteria. Implement AI to achieve a more dynamic learning system to profile customers, grant more loans and reduce bad debt. The system is able to learn and improve over time leading to a continually improving business.

Use Cases Include:

1. Profiling & Lending Strategies

Credit lending is, at its core, a big data problem. Historical loan information is utilised to calculate the loan value that a particular customer is capable of paying back. This has traditionally been done with the use of mathematical credit rules used to profile customers based on pre-selected criteria.

The generation of more data in businesses together with the implementation of AI allows for the formulation of more complex rules capable of profiling customers with improved accuracy. This allows businesses to grant more loans to more clients at reduced bad debt – if implemented correctly. The benefit of this is the improved access to credit as well as the reduction in bad debt that is taken on by businesses granting loans.

Since the rationale behind the problem does not depend on the context of the loan, this solution can be easily adapted to facilitate any type of loan in any type of industry. The key is a model that can efficiently profile users without the inclusion of bias.

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2. Loan Book Management

AI systems can replace the manual side of loan book management by leveraging over a billion transaction patterns. AI loan management systems can be divided into several use cases: credit decisions, risk management, portfolio management, personalised banking, and fraud detection.

These AI systems are revolutionising the credit lending space and assisting banks and alternative lenders to assess users creditworthiness, even those without credit histories. AI is also able to assist in making smarter underwriting decisions, streamline the lending process and even improve customer experience. AI can also be useful for creating automated alerts to companies of potentially fraudulent activities and identify key risk factors.

AI has also been applied to personalised banking and portfolio management applications where AI algorithms can forecast financial trends with speed and accuracy. These models can help financial experts to pinpoint risk areas and remove repetitive activities so that they can focus on the critical tasks. This can be extended to the personal space as to assist users to make more informed financial decisions whether it be budgeting or investing.

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