Combining analytics with AI for excellence in BFSI

Financial leaders are increasingly turning to artificial intelligence (AI) to optimize their operations. According to a recent report from Nvidia, over 90+ financial services companies are either in the process of transformation or have already adopted AI use cases for their routine operations. In this blog, you will learn how leading banks are adopting AI Analytics for operational excellence and insights for improving their profitability.

What AI brings for the financial sector?

Data analysis has been the foundation of the financial sector since the rise of early systems in the 1970s and 1980s, like SPSS Statistics or Oracle’s First Financial Services Profitability Management (FSPM) systems. However, many institutions still rely on rule-based approaches or logistics-regression models to determine credit scorings, insurance underwriting, and policies and manage liquidity risks.

As data generation floods in recent years, data analytics models have now some challenges in managing data sources, predictive capabilities, and a high rate of false positives.

Here machine learning and deep learning emerged to address the shortcomings. Unlike the rule-based approach, ML systems are more dynamic and can process data from disparate sources, identify new patterns and correlations, preprogrammed commands, and be capable of automatically executing complex analytical workflow with a high level of accuracy.

AI is a powerhouse for financial analytics

AI offers significant advantages for financial analytics compared to traditional methods. Unlike rule-based models, AI can perform multi-dimensional analysis, uncovering hidden patterns and relationships within vast datasets. This leads to faster time-to-insights, enabling quicker and more informed decisions. Furthermore, AI is revolutionizing financial analytics by offering several key advantages:

· Unveiling hidden patterns 

AI models excel at multi-dimensional analysis, identifying complex relationships within vast datasets that traditional methods might miss. This allows for a more holistic understanding of financial trends and risks.

· Speeding up insights

 AI algorithms can analyze massive amounts of data in real-time, significantly reducing the time it takes to gain valuable insights.

· Beyond predictions

 AI can generate prescriptive insights, recommending specific actions based on the data analysis. This empowers financial institutions to make proactive decisions and optimize strategies.

· Scaling new heights

 AI thrives on large datasets and can be easily scaled to accommodate increasing data volumes, ensuring its continued effectiveness.

Top data analytics use cases in BFSI

Let's review how BFSI leaders are utilizing advanced data analytics and AI applications to navigate business towards growth.

=> Customer Intelligence

Better analytics leads to better customer experience and this seamless customer experience leads to higher customer loyalty. By inducting AI/ML applications financial institutions can provide personalized guidance. AI & Analytics can be game changers for banks in understanding customers by getting deeper customer intelligence.

=> Risk management

Financial institutions can leverage machine learning (ML) models to gain a significant advantage in credit risk management. These models go beyond traditional approaches by analyzing a wider range of data, including both structured and unstructured formats. This allows them to identify early warning signs of potential defaults across the entire loan portfolio.

=> Improved credit scoring and loan underwriting

Data analytics infused with AI and ML can significantly improve credit scoring and loan underwriting for banks in several ways:

· Deeper customer insights

 Traditional models rely heavily on credit history and financial statements. AI/ML can analyze a wider range of data, including social media activity, utility bill payments, and alternative data sources.

· Reduced bias 

Traditional models can be susceptible to human biases based on factors like income or zip code. AI/ML models, when designed responsibly, can remove such biases from the decision-making process, ensuring fairer credit access for all.

· Predictive power 

AI/ML models can not only assess current risk but also predict future financial behaviour. This allows banks to identify potential problems early on and take proactive measures to mitigate risk.

· Dynamic Credit Scoring

 AI/ML models can continuously learn, and update based on new data. This allows for dynamic credit scoring, where creditworthiness is assessed in real time, reflecting a borrower's evolving financial situation.

=> Fraud Prevention

Fraud prevention is another critical area for BFSIs where machine learning algorithms can add substantial value. ML methods like probabilistic neural networks (PNN), Genetic Algorithms, Naïve Bayes classifier and support vector machines can detect financial fraud with a 95% to 98% accuracy rate.

Whereas traditional rule-based fraud detection systems can generate false positive rates of 30-70% of online transactions. In many cases, analysts manually must review and investigate false positives which brings extra cost and lower operational excellence.

=> Claim Management Improvement

AI/ML-based analytics empower insurance companies to transform advanced claim management. By analyzing vast datasets encompassing past claims, vehicle data, and customer behaviour, AI can flag suspicious activity for fraud detection. Additionally, AI automates tasks like data extraction and document review, streamlining the process for faster claim settlements.

Furthermore, AI analyzes historical trends to predict claim severity with greater accuracy, allowing for efficient resource allocation and appropriate reserve setting. AI can even leverage external factors like

weather patterns and traffic data to proactively assess risks in specific locations, potentially preventing future claims altogether.

Wrapping up!

The vast amount of data stockpiled by financial institutions holds immense potential. By harnessing this data through AI-powered decision engines, these institutions can unlock a significant edge over competitors. These advanced engines can be deployed across various financial functions, from streamlining customer experiences and preventing fraud to effectively managing risks, optimizing loan origination and insurance underwriting, and providing personalized wealth management services.

Advance Analytics with Systems Limited

Leverage the power of AI to unlock hidden insights into your finances. Partner with Systems Limited for advanced analytics that optimizes performance and empowers smarter decisions in the BFSI sector. Visit our advanced analytics page for more information.