This innovation is driving financial inclusion for people around the globe who don’t have traditional access to financial institutions. Equipped with the correct algorithm and capability to assess big data sets accurately, AI is the ideal assistant.
Using a machine learning model, AI in credit scoring will continue to develop and upgrade itself the more we use it. New advanced algorithms can be expected. AI will be able to process bigger sets of data and produce more accurate results. This means a bigger scope of potential borrowers can be accessed, while making the lenders’ work lighter.
As has been seen, this function of AI is used in real-time by several US-based finance companies, such as Ocrolus that provides financial documents review services. They’re using AI to achieve 99% accuracy in their results.
The next step to further AI’s advances is by putting more effort in training it, making it a sharper tool.
How AI is becoming essential to credit scoring
Credit scoring is one of the main ways to assess potential borrowers and help decide whether they’re eligible for mortgages, business loans, or even credit cards. It also helps determine the terms they are offered, and the amount they can borrow.
AI is essential in this area because much of credit scoring is dependent on providing financial evidence as a guarantee, usually in the form of employment payslips or assets. New potential borrowers are less likely to have assets and are in an economy where self-employed, contract, and gig work is increasingly the norm.
Then there are those who are ‘unbanked’, who don’t have any savings – that includes 1.5 billion people.
New technology means data sourcing can become broader and more inclusive. This creates new borrower categories to consider, making it possible for financial institutions to reach more borrowers who previously could not be assessed.
AI Boosts Accuracy and Efficiency
Credit scoring must be done thoroughly, and that is a process that takes time and effort when done manually.
Once the process is established, it can follow protocol and move much faster. AI’s power makes it much easier to go from identifying a new model for credit scoring to being able to roll it out reliably at scale
Machine learning means all data AI analyses feeds into the processing system. AI is trained by analysing a bulk of data consisting of transaction history, debt history, and payment history. All of which are the main points of traditional data scoring.
But, instead of only training to do this repeatedly and accurately, AI will detect previously unseen patterns. This will help predict future behaviours of potential borrowers, such as their probability of repaying on time, from groups that do not have good access to credit.
AI in risk management and assessment
When it comes to risk management, the more accurate the analysis, the better. With AI evaluating larger sets of data with more data sources, the results can be more personalised.
The model also helps the system to monitor the activities in real time using advanced and adjusted tools. Therefore, the outcome itself will always be the most up to date and precise. In a more advanced scenario, the tools can even predict based on previous patterns, giving them a function to prevent.
Real-life, real-time examples
Aside from risk assessment and data analysis, AI also contributes to many other factors. It can be used for fraud detection based on patterns that it recognises. It can also create personalised offers based on an individual’s data analysis.
The usage of this type of AI and the tools it creates is already being applied. Enova, a US-based financial technology company, uses AI to complete its credit assessment. With more advanced updates every year, we can expect even more companies in different industries utilising AI.
Future trends that must be nurtured
The biggest challenge moving forward is how much effort we want to put in to evolve the AI we have now, as the complexity grows and bigger effort is needed. Evidently, AI banking solutions help bring huge impacts, so attention is now shifted to updating them furtther.
The assistance AI brings to overall credit scoring and risk management in general will easily outweigh the complexity of its introduction. The more patterns and data AI consumes, the more accurate the results and powerful its feedback loop. Credit scoring is possibly the most impactful application of AI in financial services for the future of consumers.
- Artificial Intelligence in FinTech
