Shopping online, check. Repaying your friend for last night’s dinner, check. Managing your budget, check. You’ve likely made a few quick swipes on your phone to complete these tasks. Powered by Machine Learning and AI, Fintech apps and services today help millions of users gain access to insurance, banking, and ddigital payments. In this post, we delve into four ways Artificial Intelligence (AI) and Machine Learning (ML) help Fintechs innovate and create new opportunities for customers.

4 Ways AI and ML Are Driving Change at Fintechs

1. Powering Robust Fraud Detection Systems

$28.65 billion. That’s how much credit card companies and financial institutions lost across the world due to fraud in 2019. In the early development of fraud detection, many firms relied heavily on a statistical technique called logistic regression. Today, more potent Machine Learning algorithms like Neural Networks are deployed with advanced computing systems. The result is that they are faster and more precise in flagging potentially fraudulent transactions. Investment in such advanced systems does payoff for Fintechs and their customers.

For example, Visa estimates that its advanced fraud detection system called Visa Advanced Authorization has prevented $25 billion in fraud losses while handling over 127 billion transactions worldwide.

2. Greater Precision in Estimating Credit Default Risk

The ability to anticipate default by customers on housing or other financial loans is key to the long-term financial health of Fintechs. Additionally, it is also a regulatory requirement that firms need to report periodically.

Estimating such risk begins even before a loan is given out to a potential customer. Traditionally, consumer-lending Fintechs have relied on credit scores like FICO to estimate the risk of lending to a potential customer. However, credit scores are often available only for a limited population of a country. Take the case of China where only 20% of the population has a credit score or profile. In such situations, Machine Learning plays a vital role in estimating the risk of credit default. For example, ZestAI uses Machine Learning coupled with data from search engines and the purchase history of potential customers (with their permission) to assess the degree of risk over the course of the loan.

A further advantage of new Machine Learning algorithms is that they are adaptable to shifting business environments as compared to traditional credit scores. For example, the impact of COVID or currency devaluation can be incorporated into the algorithms to evaluate the borrower’s ability to repay a loan. Such flexibility helps Fintechs to better manage their loan portfolio and serve customers.

3. Making Wealth Management More Accessible

A recent FINRA study found that 50% of us feel stressed just talking about finances. Moreover, the services of a wealth manager or financial advisor are out of reach for most people. This is another area where AI-enabled apps, often called ‘Robo-advisors’, are pushing a revolution. Such personal finance and wealth management Fintechs are helping people get into the habit of budgeting and planning their investments. For example, AI financial assistant, Cleo, provides users with the optimal amount they can save each week.

Further, based on the customer’s profile ( like age, income, risk appetite), these AI-powered apps provide personalized recommendations for individuals that are easily accessible at their fingertips. For example, fintech Betterment provides asset allocation and tax-saving strategies for individuals based on their goals and profile.

4. Smarter Insurance

Imagine being in a car accident and receiving quick-response treatment from an AI-powered medical robot. Further, your claims are settled in just a few hours rather than a few weeks! This is no science-fiction! Rather, it’s the future of Fintech and the insurance industry in particular. Based on algorithms like Convolutional Neural Networks that mimic the functioning of the human brain, AI systems will process high volumes of real-time data generated from an estimated 1 trillion connected devices in 2025 across the world. McKinsey argues that AI will transform every aspect of insurance: pricing, distribution, underwriting, and claims. The industry will shift from a ‘detect and repair’ to a ‘predict and prevent’ mode of operation.

Many insurance Fintechs are beginning to drive this change. Take the case of the agriculture-tech firm, CropIn, which provides insight and reports for insurers engaged in crop insurance. Currently, the firm covers over 2 million farmers and 6 million acres across 52 countries. By blending Machine Learning with satellite imaging, weather analytics, and data from IoT-enabled farm equipment, CropIn helps insurers assess risk and price their products intelligently.

Conclusion

Machine Learning and AI are empowering Fintechs to be agile, nibble, and scale faster. Millions of consumers across the globe can explore financial services that they could not access or afford in the past. With advances in IoT, cloud technology, and artificial intelligence, there will be more exciting possibilities on the Fintech horizon.

If you would like to dig deeper into how Machine Learning and AI can help scale Fintech and other business verticals, visit AnyML. Our no-code Artificial Intelligence technology enables organizations and individuals to harness their key data to drive unprecedented innovation.

How Machine Learning Is Driving Innovation at Fintechs | AnyML