Tech-Driven Efficiency: How Automation is Reshaping Fintech Operations

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. 

In credit scoring, ML algorithms check data from numerous sources to build accurate credit scores. By properly evaluating prospective customers’ creditworthiness, fintechs can undertake more informed lending. ML also promotes predictive analytics by analyzing data from varied sources to predict customer behavior, attendant risks, and future market trends. 

Additional Advantages and Use Cases

Automation ensures financial processes are more efficient, accessible, adaptable, and streamlined. In turn, this leads to real-time data processing, faster turnaround times, reduction in operating costs, increased transparency as well as accuracy in adhering to regulatory compliances. These benefits facilitate better margins for fintech firms. 

The other key use cases of automation in fintech and other financial firms include:

Automatic report generation: Banks and financial institutions must follow standard requirements to generate compliance reports for any fraudulent transaction. Termed SAR (suspicious activity report), herein compliance officers had to read these reports and manually enter details in the SAR report. Thanks to NLP capabilities, automated bots can read reams of extensive reports to collate the required information and populate the SAR. 

Customer onboarding: For traditional banks and financial firms, customer onboarding is a prolonged process primarily because many documents need close verification and manual processing. However, automation captures the relevant data from KYC papers using the OCR (optical character recognition) technique. This data is then matched with the information customers have provided in their forms. 

KYC and AML norms: Both KYC and anti-money laundering are data-driven processes, making them ideal for automation. By automating manual processes, it is much easier to detect suspicious financial transactions. In this way, automation saves both time and money compared to conventional banking procedures. 

In a nutshell, by streamlining operational processes, automation minimizes the likelihood of human errors and improves overall efficiencies. Thereby, automation frees human resources from performing mundane tasks and allows them to focus on high-value work, benefitting the bottom line of fintech firms. As a result, automation is emerging as a game-changer for fintechs, helping them keep pace with the ever-evolving needs of modern customers. 

Authored By Guruprasad Baskaran, SVP – Engineering, mPokket