How to Use ML and AI in the Fintech Industry?

Artificial intelligence (AI) and its subset technology, machine learning (ML), no longer represent some futuristic innovations. From appearing as frequent tech buzzwords less than a decade ago, they have become integral to how

AI and ML tech innovations are shaped across the digital landscape. Driving innovations in certain industries, such as Fintech, AI, and ML, are particularly instrumental.

Almost all industry statistics refer to the stupendous growth of AI-powered Fintech solutions in the coming years. AI, According to a

report from Mordor Intelligence, will account for a whopping USD 26.67 billion ensuring a yearly 23.17% growth between 2021 to 2026.

As a development company specializing in the fintech industry, you already know how to use AI and ML in web development for the fintech industry. The scope, opportunities, and use cases of AI and ML in the Fintech sector are continuously expanding. Here we
tried to showcase some of these major use cases of AI in the fintech industry.

Fraud Control and Financial Security

The fintech industry remains the biggest target for most cyberattacks and cybercrimes. Since these attacks and hacking attempts are increasingly getting sophisticated, manual intervention long ago proved to be entirely out of proportion. This is where AI and
ML technologies are offering more intelligent alternatives.

Detecting anomalies, irregularities, and specific patterns common to unsolicited cyber behavior without human intervention is the biggest advantage of using AI and ML technologies to control fraudulent transactions and ensure financial security. Besides automatic
recognition of certain triggers and patterns for malevolent transactions, AI and ML can also automate specific security measures and activities for stricter control and robust safeguards.

Personalized Banking and Customer Experience through BPA

Business Process Automation (BPA) powered by streamlined multitasking machines in an environment, has now become a growth-boosting factor for many industries. Machine Learning (ML) models help machines understand certain behavior, interactions, intent, and
rules in processing transactions. Accordingly, it can help by carrying out certain intermediate steps to speed up the process. This machine-enabled ultimately speeds up customer service, eliminates human errors, and personalizes services based on customer
behavior and transaction history.

AI and ML can address customer concerns promptly by personalizing services per specific customer requirements and intent. From customer sentiment analysis to customer communication and support quality assessment to intelligent task automation to serve customers
quickly, AI and ML can facilitate customer-focused business process automation in the fintech sector resulting in greater customer satisfaction and business conversion.

Decision-Making based on Data-Driven Insights

Today’s boardrooms in any industry focus more on data-driven insights processed by analytics and business intelligence (BI) tools than human analysis. Particularly in a highly competitive and resource-intensive sector like banking and finance, decision-making
is more dependent on data insights and business intelligence tools than others. AI took this data analytics capabilities to the next level through robust exposure to a huge number of diverse datasets and parameters of analysis.

In the fintech sector, many companies primarily embrace AI for its decision-intelligence capabilities. Since the financial sector is exposed most to market volatility, fiscal turmoil, and valuation risks, faster data-driven insights processed by a humongous
volume of data are of great importance. Modern AI platforms can analyze petabytes of data across a multitude of parameters at lightning speed. This revolutionary ability to deliver precise real-time insights made AI irreplaceable in the decision-making process
of the fintech sector.

NLP & NLG Chatbots for Customer Support

Artificial intelligence (AI) has particularly been useful for customer support chatbots. Besides catching customer sentiment and intent, modern AI chatbots can also understand and communicate in natural human language. Natural Language Processing (NLP) and
Natural Language Understanding (NLG) are AI-based trained data models that help chatbots to understand human communication in natural speech and text language and communicate accordingly. Ultimately, this results in more satisfactory customer support, lead
generation, and business conversion.

On the other hand, AI chatbots going steps further than first-generation rule-based chatbots can now reply to many domain-specific custom queries, resulting in a better understanding of relationships with customers. Personalized and quicker communication ultimately
helps fintech companies to revitalize their branding on the tech landscape and generate more leads.    

Claim Management & Underwriting in the Insurance Sector

Insurance is one of the emerging areas in the financial sector where AI and ML technologies have found their footprints in recent years. Since insurance companies need to analyze many contingency factors, uncertain future predictions, and volatile financial
market dynamics, a deep-drive rigorous analysis covering a huge amount of multifaceted data is extremely important for underwriting, insurance product design, and key decision-making processes. This is where AI tools prove to be tremendously effective.

Particularly detecting fraudulent claims is a major challenge for insurance companies where AI tools can play an impressive role. Apart from the precise calculation of risk factors before the issuance of the policies, AI tools can also detect major anomalies,
irregular patterns, and incoherences in claims that need further probing by the company.

Credit and Risk Profiling for Loans

For banks and financial institutions that market loan products for different purposes, checking the credit score and making the risk profile of the customer is of seminal importance. This is another area where AI can play a tremendously beneficial role.

By analyzing a large number of datasets corresponding to individual financial statuses, demographic data, market volatility, and prospects, an AI-powered credit scoring tool can quickly develop a precise credit rating and score for a customer. This also ensures
a faster disbursal process and higher loan repayment and customer recovery.

Summing It Up

There are AI and ML in almost everything in the digital landscape. Fintech, among all the industries, is going to be the biggest beneficiary of these intelligent technologies. In the future, we can expect predictive AI inputs to help many financial institutions
to avert major financial crises like 2008 in the recent past.