Technologies like blockchain, IoT and cloud computing are driving the fintech sector to a whole new direction. But the influence of artificial intelligence and machine learning is running deeper than most – and it’s easy to see why.
Autonomous Research predicts that by 2030, AI will allow financial institutions to reduce operational costs by 22%. At present, Forrester estimates that 50% of financial services and insurance companies globally use artificial intelligence and that AI/ML use in fintech will continue to grow in FinServ.
Increasingly, banks like Goldman Sachs and JP Morgan are partnering with fintech firms or designing their own AI in finance solutions. It’s easy to see that for a financial sector business leader, the consequences of the growth of AI in fintech will be significant.
6 Ways AI and ML are finding uses in financial services
AI and ML are innovating traditional financial services and solutions, allowing for exceptional efficiency, better financial analysis and customer engagement. Six use cases of artificial intelligence and machine learning in fintech
Chatbot & Automated Customer Support
AI interfaces and chatbots are removing delays and the off-hours characteristic of traditional call centers from the customer service experience. Complete automation is not only leading to better customer experience and customized solutions, it’s also reducing human errors and accelerating back-office operations.
Smarter & Faster Decision-making
AI and ML are helping organizations make data-driven and accurate decisions in real-time. AI is analyzing the data and doing calculations instantly, which is helping finance professionals quote the most accurate numbers for products like loans and insurance. This, in turn, is helping them save time, cut costs and reduce workload.
Fraud Detection & Prevention
FinServs have always been plagued with fraud. Despite training and caution, banks still lose enormous sums of money to fraud. Digitization has only increased the problem.
AI-powered anti-fraud solutions are now helping institutions gain real-time insights into transactions. AI tools quickly analyze transaction data to detect questionable patterns or malice behaviors, making it much easier to react and prevent these activities before or as they occur
Better Trading & Wealth Management
AI applications such as smart wallets can monitor the user's behavior and detect patterns, based on which it can make recommendations for micro-investing, savings, borrowing and more. These tools are making fintech more accessible and giving users more control of their personal finances.
Client Categorization - Risk profile
Creating a person’s risk profile and categorizing them based on the assessment is a long-drawn process involving many person-hours.
Artificial intelligence tools are helping automate the process of profiling clients based on their risk profile in mere milliseconds. In turn, this is helping financial experts recommend more relevant products to customers.
Both predictive analytics and real-time analytics are helping financial experts improve sales, optimize resource use, and enhance operational efficiency. By analyzing stored customer data, AI is helping make upsells at the right moment, predicting when customers may want to leave the institution or need to renew their premium offers.
The sectors that AI in fintech will disrupt the most
The adoption of AI is helping create a better, more engaging financial environment for fintech companies. It’s facilitating new financial and banking products and driving cost-effectiveness all through the industry.
The bottom line is if you work in this sector, having a firm grasp on open banking and fintech is no longer an option--it’s a survival strategy.