Whether it’s shifting the work to a digital platform – or using the newest technologies to enhance the productivity, the BFSI segment has always been at the forefront when it comes to the adopti?on of automation. With the growth in technology follow?ed by revolution in the smartphone space and internet penetration, new allied verticals like fintech firms have cemented their position.
Artificial intelligence (AI) and machine learning (ML) are the “next big thing” in the technology world, and with the human intelligence alig?n?ing with these technologies, the results derived by different processes have seen an upward spiral. AI can process a gigantic amount of consumer data that can be harnessed for offering suitable products and services to different sections of customers. This results in high customer satisfaction as it enables financial players to find what’s right for their customers. ML learns the trends and can significantly cut the time take by different processes to increase the productivity. Here are some examples of highly relevant use cases of AI in the fintech segment.
Precise decision-making: Data-led management decisions supported by AI are not only cost-efficient but are al?so highly accurate and effect?i?ve. In such scenarios, banki?ng officials only ask for the relevant information from the machines and take decisions on recommendations by the machine.
24x7 customer support: With the advent of Chatbots-powered systems and AI-enabled voice systems, custo?m?ers can get 24x7 advice for queries at significantly lower costs and precision in significantly less amount of time.
Detection of frauds: Whi?le analytical tools procure evi?dence and process data req?u?ired for conviction, AI based tools keep a track of beha?vioral patterns to detect any signs of fraudulent attempts.
Claims management: Blessed with self-learning cap?a?bilities, AI, along with ML, can also cater to a new category of undiscovered cases and improve detection efficiency with time.
Predictive analysis: A bo?on for the financial services, predictive analytics has directly impacted the business strategy, sales growth, reve?n?ue as well as optimum usage of resources. It also enhan?c?es business processes, organi?sational productivity and co?mpetition analysis. Analytical tools operate in synchronisation with the organisati?o?ns across a string of industry verticals to garner and synt?h?e?sise data for meaningful actionable insights.
In the financial segment, predictive analysis has ena?b?l?ed precise calculation of cre?d?it scores and prevention of bad loans by harnessing huge amounts of data to locate behavioural patterns and prediction of insights. Such pred?ictions include, “what cust?o?mers are likely to buy, how long an employee will be associated with an organisati?on, and how likely is a client to default on his payment.”
Virtual financial assistance: Virtual automated financial planning and assistance help users to make better fin?a?ncial decisions by keeping a track of bond pricing, ev?ents, stocks and other fina?n?ce-re?l?a?ted events as per a consu?m?er’s financial objecti?v?es. The process also offers re?commendations related to selling or buying stocks and bonds.
Meeting compliance: Wi?th the advancement of AI & ML, financial players are rel?y?ing more on machines than analysts. As per the research firm Optimas, by 2025 AI will lead to a 10 per cent reducti?on in the workforce serving the financial sector while 40 per cent of these layoffs will occur in operations related to money management.
Also, while AI can be used to forecast future scenarios, it doesn’t depicts the reason or processes it harnesses to get a conclusion. Thus, expl?ainable AI comes as a viable solution that also cites specific reasons for certain actions like the refusal of loans.
Ensuring security: The financial sector remains the most vulnerable segment for cyber intrusions and targeted attacks, and now several AI and ML algorithms are now being developed to address this issue. As the amount of data increases leaps and bounds, developing a next-generation security solutions is becoming all the more imperative and several major financial players have already switched to machine learning-based security mechanisms. The systems analyse events and actions while simultaneously learning from a host of sources about what is safe and what is not.
In a nutshell, AI is able to create intuitive conversational interfaces like chatbots that can deliver human-like interactions and to make financial operations easier. In non-profit fundraising space, AI-based chatbot can transform the way donors interact and can communicate with multiple donors and volunteers, and help them to start fundraisers or seamlessly donate.
(The writer is founder and CEO of Crowdera)