Ritesh Srivastava, Chief Data Scientist, BharatPeBanks and financial institutions are investing heavily in business intelligence solutions to make data-driven decisions and improve customer experience while capitalizing on cost reduction opportunities. Data analysis is quickly becoming one of the most important pillars of decision-making for the the banking industry. The ability to generate real-time insights from customer data and improve risk management are some of the factors that drive customer spending. Big Data and Analytics (BDA) in the banking sector. According to an industry report, the global big data analytics market was valued at over US$240 billion in 2021. The market is expected to witness significant growth over the next few years, with an expected market value of more than $650 billion by 2029. With data being the centerpiece of decision-making, banks are able to deliver smarter, personalized solutions and distinctive experiences at scale and in real-time. AI, data analytics and automation technologies enable banks to optimize and accelerate decision-making and also help streamline their overall operations. Profit optimization with less resource allocation is the core feature of a bank’s business and data analysis, as well as a disruptive approach. AI Technologies can significantly improve banks’ ability to achieve higher profits as well as key outcomes such as large-scale personalization, distinctive omnichannel experiences and rapid innovation cycles. Here are some examples of data that permeate the different facets of the banking sector and shape its future:
Platform Operating Model/Digital Operating Models Fit for Modern Customer Experience
Banks are gradually moving towards a platform operating model that recognizes the value of AI and technology to deliver an enhanced customer and business experience. Traditional banking models, typically slower, with transactions taking several days to clear checks and deposits, are weakening as they generate fewer profits and face increasing competition from new players.
On the other hand, the platform’s operating model offers a unique advantage as it provides for business and technology partnerships, designed to focus on providing cutting-edge AI-based solutions, which ultimately become an intrinsic component of the platform. global offering of banking or financial services. business.
Banking as a Platform (BaaP) and Banking as a Service (BaaS)
Banking-as-a-platform (BaaP) and Banking-as-a-service (BaaS) are two platform-based models that enable banks to quickly transform and bring new products to more customers and geographies . While Banking-as-a-platform consists of offering new third-party services covering both financial and extra-financial products, in the Banking-as-a-service model, a bank makes its technologies and infrastructure available basic on a white support. based on labels via application programming interfaces (APIs).
With the Banking as a Service (BaaS) model, banks can focus on their strengths and offer a wider range of services to customers. Additionally, by outsourcing non-core activities in the Banking as a Platform (BaaP) model, maintenance costs are transferred to the developing business, which helps banks reduce their development time and costs.
Better understanding of access risks thanks to identity analysis
A typical Identity and access management (IAM) contains basic information about users and what they can access. However, this data does not provide access risks. In order to get a holistic view of access risks, a bank needs to have information about what users actually do with their access privileges. A modern approach to identity and access management (IAM) requires the use of identity analytics, a process that employs big data, machine learning and artificial intelligence (AI) technologies to analyze large amounts of data and summarize it into actionable insights, enabling organizations to detect and respond to access risks more quickly.
Identity Analytics makes Identity and Access Management (IAM) smarter by enhancing existing processes with a rich data set of user activities and events, peer group analysis, anomaly detection , as well as real-time monitoring and alerts, which improves compliance and reduces risk. By integrating AI technology, identity analytics becomes more robust and capable of automatically predicting trends and behaviors, and providing recommendations for corrective actions. AI uses data mining and machine learning techniques to generate hypotheses, evidence-based reasoning, and recommendations to improve real-time decision-making.
Fraud detection and prevention using advanced analytics
As online/Internet banking becomes more and more popular, cases of financial fraud are increasing year by year. According to the Anatomy of Fraud 2023 report published by Praxis Global Alliance Ltd, banking and financial services (BFSI) and e-commerce are the most vulnerable sectors, with account-related fraud claiming a 65% share in financial and banking services. 54% in financial services. e-commerce. Fraud Analysis involves the use of big data analytics to prevent online financial fraud. It helps financial organizations predict future fraudulent behavior and apply rapid detection and mitigation of fraudulent activities in real time.
Since banks and other financial institutions are responsible to their customers for securing their data and finances against fraud, deploying fraud analytics can help banks prevent financial fraud and protect their customers’ assets more efficiently than ever.
Banks and financial institutions seek breakthroughs by identifying hidden opportunities in their data that can directly impact their bottom line. They’re finding new ways to leverage data and predictive analytics to improve customer experiences and drive business growth. Given the importance of data, it’s not hard to imagine that banks of the future will build their brands on a database where every step of a customer’s financial journey is captured to better understand the experience of the customer throughout their purchasing process.
The author is Ritesh Srivastava, Chief Data Scientist at BharatPe.
Disclaimer: The opinions expressed are those of the author alone and ETCIO does not necessarily endorse them. ETCIO shall not be liable for any damage caused to any person/organization directly or indirectly.
