—TechRound does not recommend or endorse any financial, investment or monetary services or products. All articles are purely informational—
Artificial Intelligence (AI) and Machine Learning (ML) are important tools in various modern industries, including finance. Their roots trace back to the middle of the previous century when these terms were simply theories unable to be tested due to technological limitations.
Today, both AI and ML are used extensively to improve data processing, efficiency, and security. But how much do these technologies affect the payday lending market?
Understanding the Terms
AI and ML are often used interchangeably, however they are not exactly the same thing.
Artificial Intelligence
AI is a broad field that involves creating complex systems that are capable of performing tasks that typically require human intelligence. These include understanding language, recognising patterns, solving problems and making decisions. AI can have different capabilities and purposes.
For example, there are narrow AI systems that are designed to perform specific tasks, such as facial recognition or language translation. AI models like ChatGPT are more advanced and capable of more complex tasks, similar to human “thinking.”
Machine Learning
Machine Learning is a subcategory of AI that focuses on developing algorithms that allow computers to learn and make decisions based on available data. Unlike traditional programming, where a developer writes specific instructions, ML involves training a model to “teach” it to recognise patterns and make predictions. Put simply, all machine learning technologies are AI, however not all artificial intelligence systems involve or require ML.
Case Studies and Real-World Applications
Many companies in the fintech industry have implemented AI and ML technologies to improve their practice. Here are some examples:
1F Cash Advance
1F Cash Advance uses AI technology to simplify and speed up the approval process. Their ML models analyse a wide range of data to accurately analyse the applicant’s repayment ability. The approach of 1F Cash Advance combines reviewing traditional and non-traditional data to ensure they make inclusive and reasonable lending decisions without affecting a borrower’s credit score.
Fintern
Fintern uses modern AI technologies to improve their decision-making processes and make them unbiased. After you request a loan, their ML system analyses both the information you provided and data available in the credit databases to make a logical decision.
Turnkey Lender
TurnKey Lender is a global leader in digital lending automation. Their payday loan software uses custom award-winning AI to make fully-automated loan decisions. Besides fast approvals, it also improves decision-making accuracy which in turn can decrease the risk of borrowers missing payments or defaulting.
How Do AI and ML Improve Payday Lending?
The integration of AI in payday lending helps improve aspects such as application and verification processes, data verification and security.
Efficiency Improvements
AI can speed up all of the processes involved in payday lending thanks to its ability to automate tasks such as data collection, verification and analysis. ML algorithms can quickly assess the borrower’s creditworthiness by analysing the data that is provided by the applicant.
It can also verify the information for credibility by accessing online databases. This helps significantly speed up the reviewal process and help the borrower find out if they’re approved almost instantly.
Accuracy in Decision Making
AI can leverage large amounts of data and make decisions based on that. Unlike traditional methods that involve reviewing the borrower’s credit score and income, AI and ML can review alternative credit data and non-traditional financial behaviors. This can increase approval rates, reduce risks, and help the lender make the most rational and comprehensive decision.
Risk Assessment
Security is a big field where ML is currently being used. Advanced systems analyse transaction patterns to detect anomalies. Since these AIs are being trained on a vast amount of data, they can connect the patterns and draw conclusions. Security ML can detect fraudulent activities, such as various types of identity theft, loan stacking, attempts to disguise false information, and others.
Ethical and Regulatory Considerations
AI has always raised concerns regarding ethics and regulation. How do you keep it unbiased and fair? How do you protect user privacy when using large datasets for AI development and training? We do not know all the answers yet, but here are some of them.
Bias and Fairness
Bias is still a problem the developers of various AI systems struggle with. For example, it’s been shown that predictive AI algorithms demonstrated racial bias. Similarly, men are more likely to be shown ads for high-paying positions than women. These facts reflect historical data and can lead to reinforcing inequality in various industries. Ensuring AI systems are unbiased and fair requires a large amount of diverse data, transparent algorithms and regular testing and adjusting.
Regulatory Compliance
Regulatory compliance in AI requires following data protection laws like the General Data Protection Regulation (GDPR). This law states that companies need explicit consent for data collection, while the user holds the right to access and delete that data.
Besides that, the European Union’s AI Act suggests categorising AI systems by risk level and enforcing stricter requirements for high-risk models before they can be put on the market. This can help balance technological progress with safety and ethical considerations.
AI and ML are and likely will be a crucial tool when it comes to automating financial processes and making them more efficient and secure. At the moment, these technologies are used to process vast amounts of data and make lending decisions as quickly and fairly as possible.
However, AI doesn’t come without its drawbacks. Potential biases and the need for vast amounts of personal data require strict regulation and significantly more training. This will ensure this technology can improve not only payday lending processes but also overall data security and fairness while eliminating the associated risks.
—TechRound does not recommend or endorse any financial, investment or monetary services or products. All articles are purely informational—
