In a nutshell:
- Fintech companies are investing in AI to improve efficiency and enhance customer experiences.
- AI use cases in fintech include predicting customer churn, robo-advisors, chatbots, fraud detection, credit scoring, financial forecasting, and GenAI.
- AI helps fintechs streamline internal processes, build better products, and increase ROI.
- Pecan's Predictive GenAI platform simplifies building machine learning models for fintech companies.
- Fintech companies using AI are better positioned to stand out in a crowded market and deliver personalized services to customers.
According to McKinsey research, more than 20% of all organizations’ digital budgets go toward AI-related tech, and those that make significant AI investments experience a sales ROI uplift of between 10% and 20%.
Unfortunately for fintechs, while the industry is expected to grow three times faster than traditional banking companies, funding has leveled out. To succeed, fintechs must demonstrate their value to investors and customers.
As fintech leaders prioritize resources and try to stand out in an overly crowded market, AI becomes a differentiator. In this blog, we share the top ways fintechs are using AI to smooth internal processes, build better products, and increase ROI.
Photo by Jakub Żerdzicki on Unsplash
The benefits of AI in fintech
Good news: almost half of all consumers want to add more fintech solutions to their daily lives. But they also crave greater personalization and immediate customer service responses — ideal use cases for AI.
AI’s many benefits include:
- More personalization. From in-app experiences to customer service and relevant cross-sell and upsell ads, data-driven insights from AI inform how brands engage with individuals and can help make each customer feel seen.
- Improved efficiency. With AI, fintechs can do more faster, which can reduce overhead while keeping costs lower for consumers. Win-win.
- Enhanced accuracy. Fewer expensive errors in internal processes result in better regulatory compliance and higher-performing products (not to mention more accurate forecasts and budgets).
- Better security. AI can detect anomalies, bad data, and fraudulent activity before they can do significant harm.
- Quicker time to market. AI can help throughout every stage in the product lifecycle, from vetting new ideas to predicting market performance and even developing those new products at lightspeed.
Why fintech is primed for AI
While almost all industries can put AI to good use, fintech companies are especially well-suited for different AI use cases. Just consider these characteristics of fintechs:
- Large and ever-growing volumes of data, including financial data, web behavior, demographic data, and shopping insights
- Established industry domain expertise, including experts in the banking, legal, and technology spheres
- Multiple tasks of increasing complexity that won’t fit traditional rule-based automation and can change quickly over time
- Long-term projects with huge potential to create ROI
- Customers with a long lifecycle as they navigate various seasons (parenthood, mortgage, retirement) with surging expectations for personalization
Admittedly, fintech startups can require more time to get off the ground due — in part — to banking and financial regulations. However, once a project gets cleared for the public, it’s often been vetted to qualify it for the most beneficial AI use cases. A solid fintech product may have done much of the legwork to bring AI on board without too much additional oversight.
Top 7 use cases for AI in fintech
From better internal processes to delighting customers, these seven use cases demonstrate why fintech is primed for AI.
1. Predicting customer churn for financial apps
Historic customer churn rates may be calculated with a simple equation, but understanding the ongoing challenges is quite complex. Predictive AI stands in the gap between what we think causes customer churn and what is most likely to be the cause, using events like logins and search behavior as clues.
Predictive AI can also score how likely a customer is to churn so you can remedy a rocky relationship well before the customer leaves. Instead of watching customers go, you can target them proactively with personalized campaigns and more authentic support to turn them around before they’re gone forever.
2. Robo-advisors
AI trading assistants have moved investors out of stuffy trading floors and into the real world, where they can make high-stakes trades with more certainty — and from the convenience of a mobile app. Since the first robo-advisors hit the market in 2008, traders have entrusted billions of assets to this time-saving tech. (Global investors will put $1.8 trillion into these tools in 2024 alone.)
This technology doesn’t just reduce the chance of human error. Robo-advising technology gives traders and investors access to vast amounts of trading data that could never be followed manually. AI’s statistical models have also become quite advanced. With the addition of machine learning (ML), past trading wins become the training data to predict future outcomes.
3. Customer service chatbots
If given the choice between waiting 15 minutes for a human customer agent or getting a chatbot, 60% of customers would choose the chatbot. Time matters. Because people care deeply about how their wealth and assets are handled, fintech is one industry in which customers may have many questions. Chatbots can help alleviate customer service congestion and provide faster insights than a 1-1 customer service agent.
4. Fraud detection and prevention
Creating algorithms to comb through vast amounts of financial data isn't a new trend. The IRS and other government entities have been doing this for years, and it’s made a significant impact in reducing fraud.
Private companies have the opportunity (and duty) to do something similar, especially as digital money scams and fake insurance claims continue to rise. Because it’s impossible to manually review every transaction that comes through a bank or money transfer service, AI may be the perfect solution to a fraud epidemic. It can instantly recognize basic spending patterns for an individual customer and compare those to real-time transaction data, then flag any inconsistencies for an employee to review. AI also never sleeps, which can be a real benefit for combating global crimes in a 24/7 digital environment.
Photo by Avery Evans on Unsplash
5. Credit scoring and risk assessment/loan underwriting
Getting approved for a credit card or car loan used to be dictated by a credit report, income, and a few other standard pieces of information. While this qualifies the most likely consumers to repay a loan, it shuts out some borrowers who would likely be able to pay, leaving business on the table.
New AI-based credit scoring models go beyond what banks see on a static application, including the credit score at the exact moment of the credit pull. These models look at historical information from a variety of public and private sources and can predict with better accuracy if someone is truly a credit risk. This opens the door for more borrowers who have been traditionally ignored, and it keeps banks from lending money to applicants who look good on paper but are highly likely to default.
6. Financial forecasting
AI reduces the overwhelm involved with cash-flow forecasting and helps financial professionals focus on the right trends at the right time. It also flags bad data that can derail predictive accuracy. Today’s AI forecasts use the company’s own user data as well as data from third-party sources. In fact, everything from the weather to commodity values, foreign political forces, and historical customer trends can help inform better financial predictions and reduce risk over time, all with the help of AI.
7. Build predictive models faster with GenAI
Of those companies using GenAI, 82% do so because they think it will “significantly change or transform” their industries. With fintechs, large language models (LLMs) like ChatGPT4 help not only ideate but speed up how they build new products and services.
GenAI is highly prized as a coding assistant and can help your data team fine-tune existing machine-learning models and build new ones. Without the latest in GenAI tools, data analysts have to spend days or weeks researching before building a new model from scratch. Predictive GenAI can actually write code for your models in minutes; it just needs to be prompted.
Build models in minutes with Pecan
You likely already have the data needed to solve your biggest problems today — whether it’s to fight fraud, scrutinize creditworthiness, or reduce customer churn. Now, you need to have AI ingest and process it properly so you can capture those essential insights and act on that data.
With low-code predictive analytics platforms like Pecan, data professionals and business users can chat with a GenAI copilot to isolate a business problem, select the right model, and generate a SQL-based model in minutes — all in an intuitive chat-based interface.
Pecan’s Predictive GenAI platform makes building machine learning models as easy as typing. No data science or engineering degree required. You just need to be able to ask the right questions.
With the fintech field becoming increasingly crowded, those who use AI and GenAI will stand out to their customers, deliver greater personalization, and optimize their internal processes.
Pecan helps you discover accurate predictions about your business so you can make smarter investments and grow your company well into the future.
Ask us how. Sign up for a demo today.
