How to use ML and AI in the Fintech industry?

Artificial intelligence (AI) and its subset technology, machine learning (ML), no longer represent futuristic innovations. From emerging as frequent tech buzzwords less than a decade ago, they have become an integral part of how AI and ML technology innovations are shaping the digital landscape. Driving innovations in certain industries, such as Fintech, AI and ML, is particularly important.

Almost all industry statistics refer to the huge growth of AI-powered Fintech solutions in the coming years. AI, according to a report by Mordor Intelligence, will account for a whopping USD 26.67 billion, ensuring an annual growth rate of 23.17% between 2021 and 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, capabilities and use cases for AI and ML in the Fintech sector are constantly 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. As these attacks and hacking attempts become increasingly sophisticated, manual intervention has long since been shown to be completely out of proportion. This is where AI and ML technologies offer more intelligent options.

Detecting anomalies, irregularities and specific patterns common to unwanted cyber behavior without human intervention is the main advantage of using AI and ML technologies to control fraudulent transactions and ensure financial security. In addition to automatically recognizing certain triggers and patterns of malicious transactions, AI and ML can also automate specific security measures and activities for tighter control and robust security measures.

Personalized banking and customer experience through BPA

Business Process Automation (BPA) driven by streamlined multitasking machines in an environment has now become a growth driver for many industries. Machine Learning (ML) models help machines understand certain behaviors, interactions, intents and rules when processing transactions. Consequently, it may help to perform certain intermediate steps to speed up the process. This machine-enabled ultimately provides faster customer service, eliminates human error and personalizes services based on customer behavior and transaction history.

AI and ML can address customer concerns immediately by customizing services according to 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 boardroom in every industry focuses more on data-driven insights processed by analytics and business intelligence (BI) tools than human analysis. Especially in a highly competitive and resource-intensive sector such as banking and finance, decision-making is more dependent on data insights and business intelligence tools than others. AI took these data analysis capabilities to the next level through robust exposure to a large number of different datasets and analysis parameters.

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

NLP and NLG Chatbots for customer support

Artificial intelligence (AI) has been particularly useful for customer support chatbots. In addition to capturing 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 computer models that help chatbots understand human communication in natural speech and text and communicate accordingly. Ultimately, this results in more satisfying customer support, lead generation and business conversion.

On the other hand, AI chatbots that go a step further than first-generation rule-based chatbots can now answer many domain-specific custom queries, resulting in a better understanding of the relationship with customers. Personal and faster communication ultimately helps fintech companies revitalize their branding in the tech landscape and generate more leads.

Claims handling and insurance 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 contingencies, uncertain future predictions and unstable financial market dynamics, a thorough and thorough analysis covering a huge amount of multifaceted data is extremely important for insurance, insurance product design and important decision-making processes. This is where AI tools prove to be hugely effective.

In particular, detecting fraudulent claims is a major challenge for insurance companies where AI tools can play an impressive role. Apart from the accurate calculation of risk factors before issuing the policies, AI tools can also detect major anomalies, irregular patterns and inconsistencies in claims that need further investigation by the company.

Credit and risk profiling for loans

For banks and financial institutions that market loan products for various purposes, it is of great importance to check credit scores and create the risk profile of the customer. This is another area where AI can play a hugely beneficial role.

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

Sums it up

There is AI and ML in almost everything in the digital landscape. Fintech, among all 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 avert major financial crises like 2008 in the recent past.