Category
Artificial Intelligence
Banking

AI in Fintech: a Wellspring of Opportunities

Fintech companies are becoming dominant in many niches of the financial market. The same is true for AI within fintech. Learn more about the most impactful and profitable applications of AI in fintech.

Fintech is considered one of the fastest growing industries, which is hardly surprising. Anyone who’s dealt with a bank recently would be overjoyed to discover a more customer-friendly financial service, with transparent fees and superior customer care.

Transferring money through services like Venmo is a lot more convenient than any of the options provided by your local bank. Likewise, shopping for a loan using services like LendingTree is incredibly easy when compared to actually going to your bank, waiting, then dealing with the paperwork.

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.

Fintech adoption by market, 2015-2019

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.

By utilizing machine learning and complex AI-based decision systems, AI consultants can help solve most of the pressing data analytics challenges in fintech. That’s why, according to Hays, one of the largest UK recruiting companies, AI specialists will soon become one of the most highly sought-after roles for specifically for implementing AI in fintech.

With this in mind, we decided to examine AI in fintech and its potential impact on business processes and profitability.

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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.

Now, distributing more loans is one of the most significant benefits of improved credit risk assessment. And even if increased risk appetite is not your fintech company’s final goal, then it still allows you to improve your existing loan pipeline.

AI provides a tremendous advantage for companies utilizing AI in fintech for credit risk analytics. The lender can digest the wide variety of consumer data to build their prediction models instead of relying on FICO scores, rule-based loan application reviews, or legacy statistics models.

The success of AI in fintech is hard to debate. For example, Lenddo uses not only loan application data but also an applicant’s whole digital footprint to generate a credit risk score. Moreover, Lenddo’s machine learning combs through social media, browsing history, geolocation, and other external data to develop more succinct conclusions about credit risk. They claim to improve some of their clients’ credit approval rates by up to 50% due to this increased credit risk understanding.

AI applied across financial services

An Australian fintech company, Harmoney, doubled its loan offerings and extended its clients’ loan periods by utilizing machine learning in finance to process loan applications. Essentially they now know applicants better because they understand their credit score better. This means that they can trust the borrowers and offer more money for longer periods.

Likewise, underwriting can be significantly automated due to these credit risk scoring advances. Instead of manually reviewing applications, those with lower credit risk can be automatically accepted, saving time and resources. According to Allianz Global Corporate & Specialty, the vast majority of underwriting duties can be automated by 2030. Their research focuses on insurance underwriting, but this could also be true for AI in fintech given its process similarities. Services like Underwrite.AI are a step in that direction.

Lenddo and Harmoney have already onboarded AI. What about other fintech disruptors?
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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.

Price optimization is a way to win more customers for those financial companies adopting AI.
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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.

But online fraud is evolving and will always be an arms race or a game of catch-up, with fraudsters continuously developing new ways to steal information and money. AI in fintech is one possible answer to all of these threats, which is why larger financial companies are already investing in it. For example, HSBC invested in Quantexa, which saved the bank more than 7 million pounds within the scope of the pilot project by detecting and preventing fraudulent transactions through machine learning.

Rule-base vs ML-based fraud detection

Tons of bootleg solutions can be found if you’re exploring this concept, such as GitHub repos with algorithms trained on Kaggle data for credit card fraud. Large enterprises like SAS also offer fraud detection services, but you’ll have to be prepared to shell out some serious cash. However, lots of other vendors are available, with a wide variety of pricing, services, and compatibility specs.

AI-based fraud systems don’t have to rely on rules, which would otherwise severely limit their purview. They can process all incoming information simultaneously and find hidden patterns in the data. This is also called anomaly detection—when machine learning analyzes incoming data (e.g., transactions, payment requests, loan application data, etc.) and spots anomalous behavior.

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.

AI can maximize the impact of your sales activities by determining which customers are most susceptible to specific product offers. This strategy is also called bucketing, clustering, or segmenting. The premise is simple—identify people with similar purchasing habits/profiles and target each group with tailored product offers. You can build these models yourself with tools like scikit-learn, or you can find enterprise-ready solutions like RapidMiner.

Selling additional products to your existing customers is also a great way to improve their loyalty. People using more than one product from the same company are less likely to churn. This brings us to our next point.

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.

But the trick isn’t just upselling things to keep people hooked. You also need to identify people that are likely to stop using your services or financial products. Customer data contains markers of customer engagement that can point you in the right direction, such as the frequency of transactions or logins. But only AI can identify the full range of these markers and how they’re interconnected.

This use case, as with many others, has many potential vendors and solutions ready to help you fight war against attrition. Tellius offers a comprehensive AI-powered predictive analytics suite, and customer churn is just one of the use cases they support. Microsoft’s Machine Learning Studio also offers churn prediction. Numerous open-source solutions and modeling approaches are available to help you with this problem, as long as you have the expertise to pull it off. If you’re recently exploring the topic, then remember that churn/attrition problems are also called survival analysis.

AI in fintech is as powerful for customer churn prediction as for service personalization.
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The Tip of the Iceberg

These are just a few use cases for AI in fintech, as we wanted to focus on the most impactful ones. However, many other AI opportunities and applications exist in the industry. Let’s briefly go over a couple of examples.

Fintech companies in investment management might be interested in analyzing portfolios to provide better financial advice to their customers.

Insurtech companies might be interested in identifying policy risks for each new prospect so that they can focus their efforts on the best prospects.

Fintech companies that are heavily investing in marketing can utilize AI to improve marketing attribution, thereby developing a better understanding of which marketing efforts more efficiently convert people.

Conclusion

The eventual ROI of AI in your specific case highly depends on your strategic focus and exact setup. The quality of the data you collect is critical, as AI will use that to form its knowledge base. The AI learning process can be greatly hindered by errors in data or lack of data cleaning techniques.

Let’s be clear: not all of the commercially available products out there will work for your fintech company. One of the biggest reasons for this is the fact that AI in fintech is still in its infancy and vendors can't always tailor their product to individual businesses or business problems. As mentioned earlier, your readiness for AI implementation is also essential. If your data management practices are lacking, then feeding information into the system might be fruitless or even harmful for your business.

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