Financial Services

The Role of Big Data Analytics In Financial Services

The financial services industry has always depended on having access to critical information for its transactions and decision-making. Whether it's lending or investing, the organisation needs to know borrowers' repayment capacity, investors' investment goals, and preferences and risk tolerance. Moreover, the sector is highly regulated. Consequently, financial institutions need to submit comprehensive reports to the authorities periodically. This combination of data, risk, regulation, and competition has led the financial sector to be early movers in adopting data technology. And with digitisation, the volumes of available data have risen exponentially. With its transformative potential, big data is the key to gaining a competitive advantage.

Big data, an extensive and intricate collection of structured and unstructured data, is critical to addressing complex business challenges. Its analysis can unearth insights that are instrumental in navigating multifaceted issues. Big data is marked by its characteristics like volume, velocity, variety, and veracity. The velocity aspect pertains to rapidly processing new data for real-time analytics. The veracity of the data is crucial for ensuring the accuracy of insights, predictions, and decision support. Big data presents many opportunities in product design, marketing, customer service, and fraud prevention.

Big data analytics helps segment customers into groups. The vast transaction data enables financial organisations to categorise customers based on a wide array of individual characteristics. This segmentation can be based on financial behaviour, preferences, and profile, such as income level, location, or risk. By proactively addressing the needs of specific customer segments, financial firms can boost retention and reduce churn. Moreover, segmentation allows institutions to tailor their marketing and cross-selling strategies to the unique needs of each group. 

With AI analytic tools, financial services providers can utilise big data to go a step further to microsegment clients. Corporate banking institutions can classify multinational corporations into archetypes and micro-clusters based on their requirements and behaviours. Sophisticated and exciting insights emerge when machine learning models are applied to vast volumes of data comprising innumerable data points. A tech client might get classified along with a pharma client if both have deep cash reserves, as both clients are more likely to want asset management products, not credit products.

Financial institutions, including banks, are harnessing the power of big data and AI to deliver personalised services. By integrating insights and interactions, these organisations can offer highly customised services that enhance customer loyalty, reduce costs, and increase profitability. For instance, financial organisations can create comprehensive customer profiles based on channel usage, such as mobile, online, branch, or others. AI-based tools can generate personalised recommendations, which employees can leverage in customer interactions. Other examples of harnessing big data for personalisation include reviewing customer sentiment on social media to improve apps and coordinated and targeted multi-channel marketing communication, and combining credit card usage data with GPS to notify customers of offers when they are near their frequented stores.

A dearth of adequate credit history is the primary cause of denial of credit. Big data can help mitigate this challenge by enabling the assessment of prospective borrowers' creditworthiness based on payment for other services like rent and utilities. Financial institutions are integrating online payment data with other information to evaluate their customers and extend credit. Automated credit scoring is time and cost-efficient and helps avoid human biases in the appraisal process. E-commerce companies such as Amazon use big data to assess the risk of lending to new small and medium enterprises with limited public information. They analyse the historical transaction data of these enterprises on their platforms and extend credit proportionate to the sales.

Fraud in the financial sector costs businesses and customers billions of dollars annually. Financial fraud includes synthetic and traditional ID, payments, credit cards, money laundering, and automated clearing houses. Big data covers a wide-ranging and extensive dataset that allows for analysing transaction patterns. AI-powered analytic tools can identify fraudulent patterns and unusual behaviours like atypical spending patterns or transaction locations. Intelligent tools monitor data in real time, allowing for the detection of suspicious activity in time to prevent fraud. With the proliferation of online businesses, mobile apps, and cloud-based services, organisations need reliable tools to detect anomalies. AI tools applied to big data can discover contextual relationship patterns that are indicators of emerging, unknown frauds.

Data analytics in finance can positively impact other aspects like regulatory compliance, operational efficiency, and decision-support of a financial institution. Big data ensures regulatory compliance by aiding the automation of data collection and reporting. Operational efficiency can be achieved through insights that help optimise processes. Predictive analytics using big data provides visibility into future outcomes, assisting organisations in making data-driven decisi­­ons. Big data driven operations, customer service, strategy, fraud detection, compliance and risk management will lead to a more efficient, personalised, and resilient financial sector.

How can Infosys BPM help?

Infosys’ BPM in Finance group leverages cutting-edge BPM finance solutions to streamline business processes and realise optimal efficiency and accuracy across financial operations. Our expertise and comprehensive range of services help you achieve operational excellence.

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