Many financial institutions have struggled to keep up with a wave of new demands and competitive hurdles as a result of the COVID-19 pandemic.

Banking call centres are frequently overburdened, and consumers' financial requirements are becoming increasingly sophisticated. Furthermore, IT conglomerates are luring customers away with flawless digital-first card experiences.

The customer experience will evolve as AI is integrated into all aspects of the banking industry.

How can banks and credit unions handle these issues more quickly and effectively in order to increase efficiencies and drive growth? It's made possible by fintech. Many financial institutions (FIs) are considering AI and machine learning as part of their digital transformation, but developing solutions in-house could take months or years. Working with a fintech company can help you get to market much faster. It enables banks to offer new digital capabilities that improve the client experience while improving operational efficiency dramatically.

As technology advances, banks and fintech companies are rethinking ways to engage and transform the customer experience in fintech and banking. Consumer experiences are becoming increasingly dependent on the quality of interactions rather than on the items or services themselves.

Today's consumers rarely visit bank offices, preferring instead to bank on their phones, via apps, and online, moving money at the touch of a button and cashing checks by scanning them. Customers increasingly expect absolute availability, better services, smoother experiences across all channels, and more value for their money as a result of the digitization of financial services. Because the digitised nature of finance allows for a quick changeover of providers, financial institutions that fail to meet customers' expectations face rapid and increasing turnover.

How is AI delivering that?
AI is at the centre of this customer-centric product design, and it necessitates the utilisation of data to achieve a competitive edge. Companies that use data to develop their offers and improve the user experience will have a better chance of breaking into the consumer market. Some common use cases of AI in fintech are lending decision making, smart customer support, fraud detection, credit risk assessment, insurance, wealth management, and much more. AI also aids in the reduction of normal operating costs, allowing leading organisations to outperform their peers in terms of both bottom-line and top-line revenue. Technology has progressed to the point where any business can use it to power chatbots that can speak properly and quickly with customers 24 hours a day, seven days a week, or analyse customer data to acquire the kinds of insights that help them make better business decisions.

Virtual assistants and chatbots
In terms of reducing process inefficiencies and the time it takes to provide customer care, virtual assistants and chatbots have transformed the Fintech industry. Today's conversational AI technologies allow systems to recognise a user's voice, convert it to text, decipher that text, respond to the inquiry, and present it to the user as audio. Customers have appreciated the sophistication, which has also benefited call centre agents in their ever-increasing workloads. Language models such as BERT, Megatron, and others have opened up application cases in Fintech such as help desk AI, inquiry resolution, automated collections, marketing, and in-app personal support.

Processes for detecting payment fraud
Customers today expect a speedy and flawless user experience when interacting with banking software. Know Your Customer (KYC)/Anti Money Laundering (AML) checks and payment fraud detection processes were formerly major roadblocks for financial institutions in providing a hassle-free experience to their consumers. Financial institutions' manual reviews have simply added to the length of these processes. Companies have tackled the issue head-on with AI-powered real-time verification systems that can extract text from an image and match it to the user's information. Real-time payments with low false-positive rates have also been possible thanks to the application of AI approaches for transaction fraud detection, resulting in a better customer experience.

Fintech's potential to guarantee consistent availability and swiftly detect and address usability issues or malicious activity on its applications and systems is also improving owing to AI. Early fraud detection is possible using AI-driven monitoring solutions, which reduce false positives and improve the accuracy of detecting true fraud situations. Monitoring significant changes in behaviour and employing face recognition as an identification and verification process are two AI technologies for improved fraud detection.

Loans and collections
AI also makes it easier to develop and deliver additional financial services like loans and collections. Applying for loans was another time-consuming job with a significant wait. Due to AI, customers can now secure loan approval in just two days, rather than the usual 10–15 days. This can dramatically enhance customer satisfaction. In the case of collections, AI can assist in personalising the procedure to reduce friction and client discomfort. Rather than receiving random calls at inconvenient times, AI systems can recommend the optimal times to contact a consumer and also offer customised payment plans that are unique to that customer.

AI-based technology is at the heart of this paradigm shift. Artificial Intelligence is the basis on which digital financial capabilities are constructed, including capabilities such as facial identification, speech recognition, language processing, anomaly detection, and enhanced authentication processes and security. However, this is only the beginning of the banking transformation. Both at client-facing touch points and at the infrastructure level, AI has the potential to dramatically transform and improve the financial customer experience.