Fintech is considered one of the fastest-growing industries, which is no surprise. Anyone recently dealt with a bank will be pleased to discover friendlier financial services with transparent fees and excellent customer service.
Many options exist in other segments of fintech, from peer-to-peer lending to investment advice. The pace of fintech adoption is staggering, with cheaper services, flexibility unavailable in traditional financial institutions, and incredible convenience.
At the same time, banks are increasing the pace of digital transformation, and they certainly have the capital to influence the market. What's more, competition in the industry continues to grow, driven by potential opportunities.
This forces fintech companies to look for new opportunities to stand out, provide better services and improve the customer experience. They have all turned to perhaps the wealthiest competitive resource at their disposal: data. And there's a lot of it: browser parameters, transaction history, geolocation, unstructured data (images, voice), personal data provided through apps, and more.
Using machine learning and sophisticated AI-based decision-making systems, AI consultants can help solve most of the pressing data analytics problems in fintech.
With this in mind, we decided to look at AI in fintech software solutions and its potential impact on business processes and profitability.
AI in Fintech and price optimization
Price optimization can mean many different things for companies using AI in fintech. For example, your interest rate is also a price if you provide credit. If you provide transactional services, your price is the exact fee you want to charge. These uses of AI can also be called dynamic pricing.
Achieving optimal pricing in these cases guarantees many benefits:
- You don't overcharge, which increases conversion rates and client retention.
- You maximize earnings by not overpricing: you lessen the chance of losing prospects by not overpricing.
- You reduce the possibility of underpricing, which might result in missed opportunities (the amount you could have earned from a higher rate).
- Simultaneously, you lower the risk of default because excessive interest rates might diminish the likelihood that the loan will be paid in full.
AI-based fraud detection
Fraud losses on the Internet amount to billions of dollars a year. Fintech is no exception, especially for transactional and payment companies. The biggest problem with legacy fraud detection systems is that they are cumbersome and often designed as rule-based systems that only respond to a limited number of potential alerts.
However, online fraud is evolving and will always be an arms race or a game of catch-up, as scammers are constantly developing new ways to steal information and money. AI in fintech is one possible answer to all of these threats, so major financial companies are already investing in it.
AI-based anti-fraud systems don't have to rely on rules that would otherwise severely limit their capabilities. They can process all incoming information simultaneously and find hidden patterns in the data. This is also called anomaly detection-where machine learning analyzes incoming data (e.g., transactions, payment requests, credit application data, etc.) and identifies anomalous behavior.
Cross-selling and upsell with AI in Fintech
While fintech has its "wonders" offering one product, most providers offer various services. Companies want to sell these services, and your existing customer base is perfect for this; if someone is already using one of your services, they are likely to be interested in other offerings.
But which customers should you target? What products will resonate with them? The consequences can be very negative if marketing and sales are not asking these questions, and you risk alienating customers with aggressive marketing and upsell suggestions. But this is where artificial intelligence in fintech comes to the rescue, as it is ideally suited to solve such problems.
AI can maximize the impact of your sales efforts by determining which customers are most receptive to specific offers. This strategy is also called bucketing, clustering, or segmentation. The idea is simple - identify people with similar buying habits/profiles and target each group with customized offers.
Selling additional products to existing customers is also a great way to increase their loyalty. People who use more than one product from the same company are less likely to abandon it. This brings us to our next point.
Customer Retention
Improving customer retention rates should be a critical strategic goal for any company using AI in fintech.But the trick is not just to sell more to retain people. You also need to identify the most likely people 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 artificial intelligence can identify the full range of these markers and their relationships.
In this case, as in many others, there are many potential vendors and solutions ready to help you fight churn. Numerous open-source solutions and modeling approaches can help you with this problem if you have the experience and knowledge to deal with it. If you are new to this topic, remember that churn/churn problems are also called survival analysis.
Conclusion
The quality of the data you collect is critical because the AI will use it to build its knowledge base. The AI learning process can be severely hampered by data errors or a lack of data cleaning techniques.
However, not all commercially available products will work for your fintech company. One of the main reasons for this is that AI in fintech is still in its infancy, and vendors can't constantly tailor their products to specific businesses or business problems. As mentioned earlier, your readiness to implement AI is also important. If your data management practices are flawed, feeding information into the system could be fruitless or even harmful to your business.
