As the fintech sector continues to evolve, businesses are increasingly recognising the transformative potential of artificial intelligence (AI) in optimising operations and elevating customer experiences. Historically, industries, such as FinTech have thrived on transformative tech and have used it to keep pace with changing customer needs. The wider world of financial services and banking sector is no different with various new ideas taking shape to harness AI.

Almost every industry realises that AI has the potential to transform their business operations, customer engagements, and strategic goals. EY’s European Financial Services AI Survey found that 77% of European leaders in the financial services industry believe that Generative AI will have a significant impact on their operations. Like many industries, FinTech is learning how AI might shift the way businesses integrate technology offerings into financial service based companies, improve delivery to consumers and promote financial inclusion. AI specifically helps understand consumer behaviour, automate complex processes, and elevate decision-making capabilities and all critical thinking in a dynamic financial landscape. There are several uses cases for AI within fintech which are going to mature in the coming years.

FinTech is riding the automation wave

One of the most prominent areas where AI can be helpful in FinTech is automation and data-intensive tasks. In recent years FinTech players have steered their investments towards modernising payment processes and using digital money transfers to bypass the need for personal assistance. According to EY’s Global FinTech Adoption Index, 3 out of 4 global consumers now use digital money transfer and payment gateway services.

To deliver this scale of automation, FinTech has become more open to leveraging sophisticated machine learning algorithms, that analyse extensive datasets, payment patterns and anomalies beyond human capacity. This does not only minimise errors but also accelerates processes, empowering organisations to make well-informed decisions with precision and agility.

Automation of credit scoring and decision making has been available for some time now. But this automation had a serious downside. Such credit scoring or decisions are not easily explainable to the customer or within financial institutions. Why and how certain credit decisions was made or how that credit score can be improved-- solutions were not imaginative enough to explain this to the customer. But with explainable AI and AI enabled credit scoring use cases, such scenarios can be supported easily. This makes a big difference to be transparent in credit decision making.

Use Cases Enhancing Customer Experiences through Personalisation

Another avenue for harnessing AI within FinTech lies in elevating customer experiences through personalised interactions. AI-powered chatbots serve as virtual assistants, delivering tailored support round-the-clock in what ever the language. From addressing account inquiries to offering product recommendations, chatbots seamlessly integrate with voice assistants, providing unparalleled convenience and responsiveness to customers.

Another common use case is improving customer experience at contact centres.  AI is used to assist customer service personnel in summarising long history of communications within seconds and helping them to bring up past action items and critical discussion points, within seconds. AI is helping customer service assistance to search through knowledge bases and learning materials quickly and prompt best ways to address issues and scenarios in discussion with customers to improve customer satisfaction and reduce call time.

Moreover, generative AI-driven insights and robot-advisory services enable personalised financial and investment guidance based on individual investment patters, risk appetites, economic and market movements, environment and social (ESG) desires, etc. 

Use cases Optimising Regulatory Compliance with Precision

Given the stringent regulatory landscape governing FinTech, AI proves indispensable in ensuring compliance with key regulations such as anti-money laundering (AML) and know-your-customer (KYC) protocols. By automating compliance checks and flagging suspicious activities, AI systems bolster regulatory adherence while mitigating compliance risks effectively.

For instance, AI-powered platforms scrutinise vast volumes of customer data, payments and transactions to pinpoint potential AML risks, suspicious transaction activities such as anomalous transaction patterns or activity from high-risk jurisdictions. This proactive approach empowers financial institutions to thwart money laundering attempts and uphold regulatory standards with confidence.

Use cases of Revolutionising Process Improvements, Efficiency and Quality of Delivery

GenAI, an advanced artificial intelligence platform, is revolutionizing process automations across FinTech and financial services industry. In DevOps, GenAI can streamline the deployment pipeline, improve collaboration between development and operations teams, and enhance overall efficiency. By using predictive analytics and machine learning algorithms, GenAI can identify potential bottlenecks, optimize workflows, and eliminate manual errors in the software development lifecycle.

Furthermore, in environment automation, GenAI can dynamically adjust infrastructure settings based on real-time data and automate resource allocation, leading to cost savings and improved performance. In the realm of continuous development, GenAI can assist in code reviews, identify areas for improvement, and provide insights on best practices, ultimately enhancing the quality of software being produced. With its diverse use cases, GenAI is proving to be a valuable tool for FinTech sector looking to achieve streamlined and efficient processes for its customers.

Embracing Innovation through Experimentation

Lastly, fintech enterprises must embrace a culture of experimentation to unlock AI-driven opportunities tailored to their unique needs. Exploring diverse AI technologies—from machine learning algorithms to natural language processing (NLP) techniques—enables firms to uncover novel use cases that drive innovation and competitive advantage.

For instance, NLP facilitates sentiment analysis of customer feedback, offering actionable insights to refine product offerings, marketing strategies, and customer service initiatives. By continuously experimenting with AI technologies, fintech companies can stay at the forefront of innovation, driving sustainable growth and resilience in an ever-evolving ecosystem.

In conclusion, the integration of AI holds immense promise for revolutionising the fintech landscape. LTIMindtree is doing this by helping its customers to unlock new frontiers in fintech innovation. This includes identifying opportunities to streamline operations, and empowering financial institutions to thrive in an increasingly digitalised and competitive environment.