Automation has been exerting a profound impact on innumerable industries globally. In the fintech (or financial technology) segment, automation is driving unimaginable efficiencies and opportunities. Fintech mainly comprises the use of customized software and algorithms on computers and smart devices.
Although fintech has always been powered by technology, advanced technologies such as artificial intelligence, machine learning, robotic process automation, cloud computing, biometrics, natural language processing and more are now propelling the sphere to stratospheric heights.
In turn, the various forms of automation are boosting operational efficiencies, eliminating or limiting human errors, and enhancing the overall customer experience. Whether it is digital loans, e-KYC norms, biometric identity verification and digital signatures, among others, advanced automation tools are promoting greater process efficiencies.
Consequently, both new-age entities and traditional companies are deploying automation to develop novel offerings for their customers. Backed by these enablers, the fintech market in India is projected to reach $150 billion by 2025.
Benefits of Key Automation Tools
Below are some critical automation tools that help promote greater efficiency in fintech operations:
Artificial intelligence: AI is a core component of the fintech world that is used for better loan decision-making, credit risk assessment, fraud detection, and customer support as well as in wealth creation and management. For example, AI-enabled big data analytics is useful in scanning and screening immense amounts of data to make sure regulatory compliance is maintained. This is most useful in ensuring adherence to KYC (know your customer) and AML (anti-money laundering) compliances and checks. Similarly, insurers use these technology tools to improve both processing and verification of claims.
Robotic process automation: RPA is a form of automation technology that typically uses software robots and AI to perform rule-based or repetitive tasks to save money, drive consistency, and minimize errors. RPA has wide applicability across several fintech and standard finance operations such as mortgage and lending processes, banking and finance process automation, data extraction, customer care automation, e-commerce merchandising operations, optical character recognition applications, fixed automation processes, etc. Its versatile use makes RPA a champion of interoperability.
Natural language processing: NLP enjoys broad applicability in diverse domains, including finance. Supported by NLP, advanced chatbots are transforming traditional customer service in banking into conversational banking or digital banking via voice and text messaging applications as well as visual engagement tools. Automated next-gen chatbots can comprehend context, decode the sentiment behind the text, and perform predictive analytics tasks.
Machine learning: ML has many key applications in fintech. This includes fraud detection, credit scoring, predictive analytics, and more. For detecting fraud, ML algorithms analyze transactions, detect unusual behavior, and highlight potentially fraudulent activities. Timely fraud detection helps fintechs prevent financial losses by protecting customer assets.
