Artificial intelligence technologies are now widely used in the financial industry, especially for managing credit risk.
AI is widely adopted by innovative fintech companies. According to Market.us, the global market for artificial intelligence in financial technology is now estimated at $14 billion.
The same source predicts it will grow to $76 billion over the next 9 years.
AI is popular because it offers many benefits to businesses. However, using it comes with challenges. One main issue is that many AI-based credit scoring models work like a “black box,” making them hard to understand.
Blackbox AI refers to machine learning models whose decision-making processes are difficult or impossible for humans to understand and interpret.
The process of using this type of artificial intelligence involves the following steps:
1. The system receives data to assess the risk.
2. The program runs the necessary algorithms.
3. The user gets the result.
As you can see, with the help of AI blackbox you will be able to assess the level of risk, probability of default, or other parameters.
However, you won’t be able to understand why the system gave that particular result.
Despite its lack of transparency, blackbox AI can significantly improve credit risk assessment.
It helps credit organizations achieve the following results:
Faster verification process. Blackbox AI models can handle large data sets much quicker than whitebox AI models or manual methods.
High level of autonomy. The lender doesn’t need to use human resources to configure or adjust the system, as all decisions are made automatically without human involvement.
Strong learning ability. Blackbox AI models continuously self-learn, allowing them to quickly adapt to new fraudulent tactics.
Because of its nature, implementing black box artificial intelligence in the lending industry can face some challenges.
1. Architectural complexity. Blackbox AI, such as deep neural networks and natural language processing models, have complex structures with many parameters. This complexity makes it hard to track how inputs lead to specific outputs.
2. Lack of transparency. Finance is one of those areas where the validity of the decision made can be critical. Unexplained credit scoring results can reduce user and stakeholder confidence.
3. Compliance problem. The main concern is that blackbox AI does not comply with regulatory requirements. These regulations state that machine learning models used for credit risk assessment must be transparent and explainable.
These challenges are preventing financial organizations from fully adopting AI for automated credit risk assessment. However, they are still successfully optimizing their processes with blackbox AI.
As part of improving risk management policies, blackbox AI can be used for the following purposes.
Machine learning models use a wide range of alternative data to analyze and determine which of them can change the level of risk. This does not contradict regulatory requirements.
This significantly improves the accuracy of risk level prediction by establishing a clear distinction between the characteristics of favorable borrowers and defaulters.
With the help of blackbox artificial intelligence, when assessing a potential borrower, it is possible to take into account activity in social networks, purchasing habits and even character traits. All this allows lending to unbanked and underbanked persons.
AI-based scoring system is able to quickly analyze large data sets and automatically issue a decision on the appropriateness of lending. This is a great way to increase the number of transactions processed.
Automated credit scoring models constructed on blackbox AI allow dynamic monitoring of consumer activity. Unlike traditional scoring models, they use up-to-date information about the applicant.
The alternative to black box artificial intelligence is white box, or explainable AI.
Whitebox machine learning models offer clear and transparent decision-making processes. This allows humans to easily understand and interpret them.
The RiskSeal digital scoring system uses explainable AI to evaluate potential borrowers. We don't just provide our clients with the result of the check. We give them access to the formula that explains the result.
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