AI in Fintech: a Wellspring of Opportunities

Plenty of options also exist in other fintech segments, from peer-to-peer lending to investment advisory services. Fintech adoption rates are astounding because of cheaper services, flexibility that’s not available through traditional financial institutions, and incredible convenience.

At the same time, banks are ramping up digital transformation, and they certainly have the capital to impact the market. Moreover, competition within the industry continues to grow, driven by potential opportunities.

This pressures fintech companies to search for new opportunities to stand out, provide better services, and improve customer experience. That’s why they’ve all turned to perhaps the most abundant competitive resource at their disposal—data. And there’s plenty of it: browser specs, transaction history, geolocation, unstructured data (images, voice), personal data submitted through applications, and much more.


But data analysis in fintech has moved far beyond legacy systems, spreadsheets, and pivot tables. So what’s the secret ingredient? Artificial intelligence drives most innovations in fintech analytics and product development. In fact, most fintech investment professionals consider it to be the most impactful technology in fintech right now.

While AI is hardly a magic pill, people have good reason to find it so impactful. It’s likely that AI will add $13 trillion of global economic activity across multiple industries by 2030, with financial services being one of the prime benefactors. With many services being digital-first, advanced data management and analytics are in their pedigree. Yet the challenge is not exclusive to AI in fintech, as other industries are also overwhelmed with data and looking for novel ways to make sense of it.

Credit Risk Scoring and Underwriting by AI in Fintech

Most companies utilizing AI in fintech specialize in services reserved for traditional banks, which is why lending and financial management are the biggest industry niches. In the United States alone, outstanding consumer credit debt approached 4.2 trillion dollars according to the Federal Reserve System as of January 2020. That’s an excellent opportunity for alternative lenders.

This means that credit risk is incredibly ripe for AI applications. It’s in high demand, it’s historically based on consumer data analysis, and it directly influences profitability. All of this is even truer for companies working exclusively in lending.

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AI in Fintech: Price Optimization

Optimizing prices could mean many different things for companies utilizing AI in fintech. For example, if you lend, then your interest rate is also the price. If you provide transaction services, then the price is the exact fee that you’re looking to charge. These AI use cases can also be referred to as dynamic pricing.

Achieving the optimum price in these use cases guarantees many advantages:

  • You do not overcharge people, thereby improving conversion and retention rates. In fact, for banks, 39% of customers will likely switch to a different provider because of better pricing, according to Bain & Company. The same is true for most lending and mortgage companies in fintech.
  • You maximize profits: by not overcharging, you reduce the risk of losing prospects.
  • You minimize the undercharging risk, which translates into potential lost opportunities (the amount you might have earned through a higher rate). At the same time, you’re lowering default risk, as sometimes exorbitant interest rates decrease the chances of the loan being paid out in full.

In a similar way as credit risk, AI utilizes historical data to deliver results in this use case. However, these problems can be solved through different machine learning algorithms (regression, classification, etc.) depending on your specific implementation strategy. Pricing can be tackled in a variety of ways. Publicly available research papers detail how you can predict the optimal price for your financial services. You can also build price predictions by utilizing established providers, such as AWS and their machine learning capabilities.

AI-based Fraud Detection

Online fraud losses amount to billions of dollars per year. Fintech is not an exception, especially for companies handling transactions and payments. The biggest problem with legacy systems detecting fraud is that they’re cumbersome and often designed as rule-based systems that only react to a limited number of potential red flags.

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Cross-Sell and Upsell with AI in Fintech

While fintech has its one-hit wonders offering a single product, most providers have a variety of services available. Companies want to sell these services, and the existing customer base is perfect for that; if someone is already using one of your services, they’ll likely be interested in other offers.

But which customers should be targeted? What products will resonate with them? The consequences can be very negative when marketing and sales teams don’t ask these questions, and you risk alienating customers with aggressive marketing and upsell offers. But that’s where AI in fintech steps in, since it is perfect for these types of problems.

Customer Retention

Churn rate is one of the biggest KPIs for fintech startups, especially those in the early stages of funding. Improving retention by just 2% is shown to increase revenue by the same amount as cutting costs by 10%, according to SuperOffice. That’s why improving retention rates should be a key strategic goal for any company using AI in fintech.

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