It is now the turn of artificial intelligence (AI) to set off a new cycle. Just like computers, this has also been marked by apprehensions of human functions being substituted by non-human skills. But as AI adoption begins to move away from the labs to the wide world outside, there is increasing realisation that artificial intelligence may not be as bad as it was feared to be. Rather it could mean a new world of opportunities and greater efficiency.

Thanks to AI, the world of finance is going through a paradigm shift. With the proliferation of more digital devices, a slew of data generated products and services promise the creation of new possibilities. Today, AI is reshaping the way financial institutions generate and use data insights. This usage of intelligent algorithms to offer improved financial operations and solutions has resulted in the genesis of fintech, the portmanteau developed using the words ‘financial’ and ‘technology’.

Fintech helps firms manage their financial practices more effectively. Financial incumbents that offer services in banking, insurance, risk management, and trade are all using fintech to enhance their operations. It must be evident by now that fintech is not powered by AI alone. Technologies like blockchain, robotic process automation, and big data analytics also contribute to the fintech movement. Crowdfunding platforms, mobile payments, cryptocurrencies, blockchain, insurance, and budgeting applications are all examples of fintech which are now in use.

The adoption of AI algorithms has moved from the periphery to the central part of fintech. With data as a core component, the disruptive nature of AI brings enormous potential gains. Fintechs are already looking into better methods to leverage data and AI in their services and products so that they can provide relevant, trustworthy insights, suggestions, and controls.

One of the primary uses of AI in fintech is to find anomalous transaction patterns in data that may indicate the presence of fraudulent activities. Prior to the AI-based approach, financial institutions leveraged a rules-based approach, which required manual work and human supervision).

Today, businesses employ supervised and unsupervised machine learning to train models so that they can detect fraud attempts faster than they can use human (rule-based) methods.

Fintech also uses chatbots that specialize in addressing consumer queries regarding their current balance, previous spending, and transaction history. Chatbots convert complicated business interactions into simple chats using natural language processing, conversational AI, and machine learning technologies. Some chatbots provide answers to client queries about investments, trends, savings, loans, and insurance plans, among other things, to help customers keep good track of their financial situation.

In general, these chatbots are given inputs comprising keywords and sentences containing the relevant phrases that will be processed during the training phase. For instance, for a chatbot feature offered by a bank, the chatbot training data would contain the normal queries that come while making a transaction like, amount to be sent, payment status, etc. When data is acquired from customers, it is tagged with annotation services to make the key phrases comprehensible to computers. This allows computers to learn from the conversation and respond appropriately.

KYC and identity verification are other important areas where AI can be very effectively used. Earlier, humans had to manually verify if the given documents of customers are accurate or not. This tedious task used a lot of time and resources. Now, AI can assist banking applications and other online financial services in automatically and securely verifying clients’ identities. This is referred to as KYC. For example, to prove identification online, companies can use computer vision and cross-check if the input picture or a snapshot of the ID card of a customer is authentic.

Financial institutions like banks can leverage AI for the profiling and assessment of clients based on their risk scores. Through automated machine learning, artificial neural networks, these firms can assess borrowers with little to no credit information or history. By analyzing thousands of data points, lenders can get transparency that conventional underwriting systems cannot. This allows them to properly categorize groups as per their ‘at-risk’ levels. Then based on this classification process, advisors can opt to connect financial products with each risk profile and offer them to customers via targeted and automated recommendation systems. By more accurate prediction of risks, such profiling work helps lenders cut losses significantly.

In spite of advances in the fintech business, frauds have long been a source of concern, particularly in the financial and banking industries. To mitigate this, fintech firms use AI tools to gather evidence and analyze data, where they study and monitor user behaviour patterns to spot fraud attempts and incidents that are unusual. With their self-learning abilities, if any suspicious activity is detected, AI algorithms can not only alert companies about potentially fraudulent transactions but also enable taking steps to avoid them. In case a pattern deviates from the confidence band, the issue is assigned to a human employee who could make a rational decision. Such use of AI can also help government bodies identify corruption networks.

AI is already playing a significant role in the fintech sector and it will continue to do so in the future. While its existing algorithms cater to a wide range of activities, they are continuously learning from massive amounts of data and closing the gap by moving the industry closer to a totally automated financial system. Hence, companies are investing in AI-powered fintech to augment their financial capabilities and increase their return on investment. (IPA Service)