FinTech has completely revolutionised the way people manage money today. From paying bills to getting loans and investing in the stock market, everything is at your fingertips. It won't be completely wrong to say that the industry has become a significant part of a common man’s life today, without which they may struggle to do their daily tasks.
But what's powering modern fintech? Plenty of technologies like blockchain, IoT, Artificial Intelligence (AI), and Machine Learning (ML). But we’re going to specifically focus on AI/ML today.
These technologies help the fintech app understand user needs, prevent fraud, and offer personalised financial advice.
This guide will help you understand how AI and ML are shaping the fintech industry and making complex processes like investing, lending, and saving efficient for its users.
Let’s Go!
Fraud Detection And Prevention
The fintech industry deals with big money; thus, it is on the radar of cyberattackers who try various methods, including phishing, money laundering, or unauthorized transactions.
How does AI help in fraud detection?
Take an example of a user who makes multiple transactions with his credit card. He will get a call from the bank to verify if the payments were made by him.
Or imagine a user is in Mumbai, but his card was used in Dubai. He’ll get an immediate notification. This is such a good way to save the users from any financial fraud.
Without AI and ML, this would not have been possible. These systems keep a track of the user’s financial history and alert immediately if they sense any suspicious activity. It’s the pattern of transactions that helps.
Examples: PayPal’s AI scans millions of payments per second, blocking fraud that costs the industry $40 billion yearly. In India, Razorpay’s ML catches sketchy UPI attempts like duplicate payments, saving merchants lakhs.
Credit Score And Risk Assessment
AI and ML have made the task of getting a loan easier. These systems allow AI fintech companies to look for alternate data sources like transaction history and bill payments to generate a credit limit.
Examples: Digital lenders like Upstart and Zest AI use AI-based credit scoring to approve more applicants safely. FinTech companies like Razorpay are leading the way in using ML to improve payment security.
Personalized Financial Services
AI and ML allow the fintech apps to track user behavior, transaction history, and lifestyle habits to deliver personal financial goals. These apps can suggest a saving plan based on the user’s behaviour and income to meet the requirements. They can also suggest a good investment plan based on the user’s risk tolerance.
Examples: Groww App in India uses machine learning to track user behavior. How much users invest, when they withdraw, what are their preferred sectors. Based on the behaviour, it offers custom investment insights, like portfolio rebalancing suggestions or fund alternatives that suit their risk profile.
This makes stock and mutual fund investing personalised and beginner-friendly.
Algorithmic Trading
Investing in the stock market is rewarding but comes with its share of risks. Proper knowledge about the market is a must to gain maximum benefits; however, becoming a successful trader is not everyone’s cup of tea.
This is where AI and ML can be of great help. The AI trading systems analyze millions of data per second, including stock prices, economic indicators, news sentiment, and even tweets.
ML algorithms then make high-frequency trading decisions to maximize returns and minimize risk. These systems continuously learn from market patterns, adapt strategies in real-time, and often outperform traditional trading methods.
Examples: India’s largest stockbroker, Zerodha, integrates with a platform called Streak that lets even non-coders do algorithmic trading.
Here, the user can set conditions like: “Buy this stock if it drops 5% in 15 minutes and RSI is below 30.” The system will track those conditions using live data and execute the trade automatically when they’re met.
ML models in the background can help fine-tune the strategy over time. With the AI in fintech markets worldwide growing rapidly, more platforms are now offering retail investors access to AI-powered trading tools.
Chatbots and Customer Support
Customer service in fintech is expensive due to repetitive queries and long wait times. AI chatbots can handle queries and reduce the human load. Natural Language Processing (NLP) enables these bots to understand user intent and deliver accurate responses.
Advanced ML models allow the bots to learn from interactions, becoming smarter and more helpful over time.
Examples: Erica by Bank of America helps users track spending, pay bills, and get financial tips. Additionally, Cleo and Plum are AI-powered financial assistance for millennials, offering witty responses and budgeting help.
Final Thoughts
AI and ML have given extraordinary powers to the fintech industry.
From fraud detection to personalised saving and investment plans, these technologies have revolutionised how financial services are delivered, making them more efficient, secure, and user-centric.
The rise of AI in fintech is helping companies create smarter, user-first financial products, and the businesses need to hire AI developers that will incorporate it will be in a better position to lead tomorrow's financial revolution.
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