Data Analytics Use Cases
Data Analytics / March, 14 2022

The 6 Definitive Data Analytics Use Cases in Banking and Financial Services

Financial services organizations were traditionally product-centric, but they turned to be customer-centric with the evolving tech landscape. Digital transformation in financial services is more profound than leveraging data and digitization.

Personalization and customer experience now deepened after the pandemic. With the shift towards mobile, almost 89% of customers now prefer mobile banking channels, and digital-only banks are transcending traditional banks. Unlike financial services organizations, fintech start-ups leverage technology and data analytics as per customer preferences.

Intense competition and tech disruption are the game-changer for fintech. Let us talk about the example of loan disbursal. Banks have a lot of KYC and due diligence processes that delay the loan disbursal. But data analytics and AI make it easier for fintech start-ups to decide in minutes. Many leaders were leveraging compelling data analytics use cases in banking and financial services. But they have to be updated regularly with the evolving tech landscape.

Why do banks need to leverage advanced analytics?

Customer experience is the new competitive battleground for banks and financial services – In traditional banking business models, customer service was synonymous with customer experience. But now, ease of access, ease of use, and resolutions in no time seems to be the new face of customer experience. The financial services industry has more challenges with the data flow from these multiple channels with omnichannel presence.

AI is critical in the new CX – The applications are manifold in financial services – chatbots, AI-powered automation, and AI data analytics. Predicting the customer needs, providing services, and resolving queries in no time enhance customer experience, the new norm for financial services.

Data analytics is not about cutting costs but focusing on productivity – Do you know that leveraging advanced data analytics for fraud detection can save costs up to 20%? Earlier it was just automation of a document management system or repetitive processes. But now, it is more about leveraging technology for credit modeling, risk analysis, and fraud to leave humans for more critical projects.

Advanced Analytics in BFSI – Benefits

Updating the data analytics use cases in banking and financial services with the evolving data science methodologies can help organizations sustain stronger customer relationships. Let us look at a few more benefits of advanced analytics.

Customer 360-degree insights – By leveraging advanced analytics, financial services organizations can know more about customer preferences, multichannel touchpoints, and buyer behavior factors. There is a high chance that the sales folks might perceive a different need, but the data speaks another consumer behavior. Understanding the customer in detail is critical for banking and financial services, unlike other industries.

Personalized customer experience – Experts perceive personalization as another critical aspect in BFSI to reduce churn and improve revenues. Offering the right product at the right time while also reaching out with personalized information after understanding every consumer detail is now the norm for sales teams in BFSI. A report from Forrester says that a single point improvement in financial services organizations’ CX score can improve revenues from $5-$123 mn.

Reduction in operational costs – Banks and financial services organizations are under constant pressure to maintain sleek profit margins and improve operations. Financial services firms can leverage predicting analytics, visualization, and AI to automate their workflows. Replacing paper-based forms with digital applications and using NLP technologies where ever necessary also helps in reducing manual efforts and errors.

Risk mitigation – The main challenge for BFSI firms is to analyze risks like credit, claims, and fraud. Though the practice is not new, banks, insurance companies, and investment bankers need to update their risk approach with the evolving technologies and exploding data from multi-channels. Financial services organizations can modernize their risk management practices more efficiently using predictive, behavioral, and advanced analytics.

Competitive advantage – Fintech organizations with technology as their core are already disrupting financial services. Financial services organizations now need to adopt technology faster than before. Processing a loan application can be done in minutes with AI and advanced analytics, thereby providing more scope for customers. Data analytics in banking will enable you to understand the unmet customer needs and help you unfold new consumer-centric business models.

Best Definitive Data Analytics Use Cases in Banking and Financial Services

Most of us know about the data analytics use cases in banking and financial services. Do we need to view them differently after the pandemic? Our experts say that the customer data is changing rapidly, and so are the touchpoints. To successfully implement data analytics in banking, the models should reconsider all the available data from the expanded sources. Let us rethink the advanced analytics use cases with the changing consumer ecosystem.

Credit Modeling – Credit risk modeling is not new in the banking industry. The traditional risk analytics models provided insights based on income sources, loan history, default rates, credit rating, demographics, etc. Many other factors need to be analyzed in conjunction with the standard data. Let us consider the case of consumer loans; different dynamics like social media profiles, utility bills, monthly spending, and savings give more profound insights into the default risk. Unstructured data plays a vital role in credit risk modeling too. AI-based text analysis and consumer persona provide deeper insights into the customers’ financial well-being.

Risk Analysis and Monitoring – Banks and financial services organizations that implement dynamic risk models with advanced analytics seem to be more resilient to significant external changes. Risk models differ between Banks and financial services – credit risk, fraud, and liquidity risk are the major ones for banks; claims risk and fraud for insurance and portfolio risk analysis for investment bankers. The common risk for most financial services firms is fraud detection is continuously evolving. Machine learning, AI, and big data now enable organizations to analyze many transactions, not just based on historical data. Social media profiles, behavioral analytics, predictive analytics, and advanced machine learning models are leveraged collectively for fraud detection.

Customer LifeTime Value – The trickiest one but looks like the most simple one to understand for anyone in the banking perspective. Customer lifetime value provides insights about the future revenue sources from the customer to focus marketing efforts and reduce churn. It is tough to estimate how customer behaviors change with time and the significant factors impacting their decisions. AI-powered advanced models recognize patterns more effectively in the data to provide behavioral insights that humans may not be able to identify.

Product Recommendation Engine – Are we talking about retail? No, product recommendation engines are evolving in banking too. Multiple comparison sites are now available for each financial services product – loan, insurance, mutual funds, credit cards, etc. Consumers can make informed choices, but cross-selling financial products at the right time cater to customer needs and enhances trust. Machine learning models process data in real-time from various content feeds to make the job easier for financial/investment analysts to offer personalized products and services.

Customer segmentation and personalized marketing – Understanding every aspect of the customer is critical for personalization. Customers are now bombarded with different financial products at the same time. How do you know if a customer is looking for an auto loan? Does the customer intend to purchase a home or an automobile? The place and timing of your marketing efforts matter in creating trust and showing intent to act on the marketing messages. You can also reduce awareness marketing efforts if you provide the knowledge at the right stage of the buyer journey.

AI-powered Virtual Assistants – Consider the case of insurance; a loss or damage may not happen multiple times. It is the single touchpoint to show the customers how you care for them and ease the processes. Customers now prefer efficient self-service options to in-person contacts to process their requests. AI-powered virtual assistants add value in answering all the information queries about products, services, and eligibility criteria in financial services. They are also evolving to validate certain criteria based on the rules updated with the machine learning models. It wouldn’t be a surprise to see that an AI-powered assistant does the insurance claims processing in minutes.

Are you interested in more data and analytics use cases in banking and financial services? Get in touch with our experts to get the mind share.

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