16 min read

How is data analytics used in the banking industry?

Banking

BI & data analytics

FinTech

Banking data analytics is all about gathering and analyzing data to help financial institutions make informed decisions. By digging into customer transactions, market trends, and risk assessments, banks can uncover insights that shape their strategies and gain a competitive edge. Data analytics in the banking industry is expected to grow significantly – from US$8.58 million in 2024 to US$24.28 million by 2029 – with a strong annual growth rate (CAGR) of 23.11%.

In this article, we’ll dive into how data analytics is shaking up the banking world, making operations smoother, decisions smarter, and growth faster. Ready to see how it can make a difference for you? Let’s get into it!

Why do banks need data analytics?

Data analytics has been a big deal in banking for a while now — in fact, banks are actually seen as pioneers in using it. But to really get the most out of banking data analytics, it needs to be part of everything, from customer insights and risk management to finance and operations. When all the pieces work together, that’s where the magic happens. It helps banks stay on top of regulations, manage risks better, and fight fraud more effectively. Plus, it can drive profits by finding high-potential customers, improving product offerings, and helping leaders make informed decisions across the board.

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Key areas of banking data analytics

Risk analytics and credit scoring: 30% of use cases

Data analytics gives banks a much sharper eye for spotting risks and understanding their financial impact. For instance, “what-if” models let them play out different scenarios — like currency or commodity price shifts — so they can fine-tune their hedging strategies. When it comes to assessing creditworthiness, it looks at a wide range of insights — like spending habits, income trends, and repayment history. Combined with ML, it adds another layer by spotting risk patterns and giving even more accurate credit scores.

Fraud detection and prevention: 25% of use cases

Advanced banking data analytics helps banks to dive deep into transaction data and customer behavior to pick up on any abnormal activities that might otherwise go unnoticed. With real-time monitoring in place, banks can quickly catch and respond to fraud attempts and protect both their customers and their business. Thanks to tools like AI, advanced segmentation, and RPA, banks are moving from old-school, guesswork methods to more accurate, behavior-based techniques that improve fraud control.

Personalization, NBA/NBO: 20% of use cases

Data analytics in banking gives banks a complete picture of each customer by pulling together data from different touchpoints to create detailed profiles. Banks use next-best actions (NBA) and next-best offers (NBO) strategies to improve customer satisfaction and find more chances for upselling and cross-selling. By integrating offline micro-moments analysis, banks can use customers’ offline interactions to personalize their online experience, and vice versa — creating a smooth and engaging customer journey.

Improving operational efficiency: 15% of use cases

Banks use internal databases, CRM systems, social media, and market data to track key metrics – like cost-to-income ratios, return on assets, customer acquisition costs, and process cycle time. These KPIs help measure performance, identify inefficiencies, and guide optimization efforts to improve overall operations. Data analytics also comes in handy for performance benchmarking, where it compares a bank’s metrics to industry standards, highlights gaps, and points the way to improvement.

Marketing: 10% of use cases

With data analytics, bank marketers can easily spot trends and insights about new and existing customers. By analyzing data like customer engagement, spending habits, and behaviors, banks can build targeted strategies that make their marketing efforts more effective. Now, thanks to data streams and analytics, marketers have all the info they need at their fingertips. Data analytics also helps analyze the effectiveness of marketing and retention campaigns by measuring conversion rates and return on marketing investment.

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Data analytics in banking industry: key integrations

No matter what you’re aiming for — stopping fraud, improving marketing efforts, or managing finances — bringing data analytics into your systems and processes is a smart move that equips you with valuable tools across your entire banking framework.

Core banking systems

Banks can integrate data analytics with core banking systems (CBS) to strengthen risk management, improve operational efficiency, detect fraud, and analyze transaction patterns.

Banking CRM

Banks use data analytics to build unified CRM platforms that help identify opportunities, estimate revenue potential, give pricing guidance, and spot clients at risk of leaving.

Banking operations management system

When integrated into banking operations management software, data analytics helps banks track KPIs, gather real-time data, and create feedback loops to fine-tune service strategies.

Security and compliance tracking tools

Data analytics helps banks monitor correspondent behavior, minimize high-risk transactions, flag suspicious payment instructions, and strengthen customer due diligence and AML efforts.

Client-facing apps

Integrating data analytics into client-facing banking apps helps deliver personalized financial services and advice by analyzing customer behavior, preferences, and transaction history.

Accounting or treasury system

Banking data analytics helps accounting teams compile financial statements and effectively spot and fix mistakes such as misclassifications, duplicate entries, or data entry errors.

Financial data marketplaces

With data analytics, banks can access large datasets from social media, eCommerce transactions, and mobile devices to gain more accurate and reliable market insights.

Credit rating bureaus

Thanks to advanced data analytics, credit rating bureaus can better understand customers’ creditworthiness, spot potential defaulters, and offer more inclusive credit options.

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Key benefits of data analytics in banking

Banking operations are deeply connected to financial figures and information. When you bring data analytics into the mix, you get access to more accurate and detailed insights that help you drive more effective strategies.

Better customer experience

Improved risk management

Operational efficiency

Regulatory compliance

Strategic marketing and sales

Better customer experience

Data analytics provides valuable insights into customer segments, interactions, transactions, and feedback, giving banks a clearer understanding of customers' needs. This allows for more personalized services, increased customer satisfaction, and reduced churn.

Improved risk management

Banks use data analytics to build models that predict future risks by analyzing past data with advanced stats and ML. This helps them come up with strategies to tackle potential problems before they can cause any real trouble.

Operational efficiency

Data analytics helps banks figure out the best staffing levels, spot operational hiccups, and understand transaction volumes. With these insights, they can fine-tune how they use resources, streamline their processes, and cut down on inefficiencies and costs.

Regulatory compliance

With data analytics, banks can keep a close watch on compliance and automate the process of pulling and analyzing data. It helps generate accurate, complete reports that meet all regulatory standards, saving banks time and money while keeping them fully compliant.

Strategic marketing and sales

Data analytics helps banks spot market gaps and create marketing strategies that hit the right notes. By figuring out what’s missing, they can design products and services that really meet customer needs – leading to more effective sales efforts.

Better customer experience

Improved risk management

Operational efficiency

Regulatory compliance

Strategic marketing and sales

82% of companies saw steady revenue growth over three years.

54% of companies reported a boost in their revenue.

44% of businesses outpaced their peers.

42% of organizations saw big savings on costs.

“Data is the secret sauce to success in any business, and it’s especially crucial in banking. With the right data analytics, the possibilities are endless — like predicting customer needs, transforming credit scores, supercharging sales efficiency, and tightening up fraud protection. We’re here to show you how data analytics can take your business to the next level and help you use best practices and tools to get actionable results.”

Siarhei Sukhadolski

FinTech Expert at Innowise

Challenges of integrating data analytics in banking

Data privacy and security

Handling sensitive data in analytics is tricky — data breaches and unauthorized access can lead to serious legal, reputational, and financial headaches. Banks need to have strong protections in place, like encryption, tight access controls, secure storage, and data anonymization. Plus, sticking to rules like GDPR and CCPA is crucial to keep customer data safe and sound.

Data quality and accuracy

The banking sector deals with a lot of complex data from various sources, so it’s crucial to keep it accurate and complete. Poor quality data can result in misleading insights and bad decisions. To fix this, banks should use tools like data lakes and warehouses to consolidate everything, and rely on data validation, lineage tracking, and quality checks to keep things in order.

Integration with legacy systems

Legacy banking systems often can’t keep up with the huge volumes of data and struggle to work with modern tech. To tackle these issues, banks should upgrade their infrastructure or connect their old systems with cloud-based systems via APIs to enjoy the perks of data analytics without the high cost of a full system overhaul.

High implementation costs

Implementing data analytics in banking can be pricey due to the complexity of the projects, the need for advanced tools and expertise – plus costs like licensing fees and team training – making it a big budget issue. Using cloud storage like AWS, Azure, and GCP offers scalable, cost-effective solutions, while data compression can cut down on storage and transfer costs.

Regulatory compliance

Ignoring data security rules like GDPR, PCI-DSS, Dodd-Frank, Basel III, and FATCA while setting up banking data analytics can result in hefty fines and loss of customer trust. Banks need to stay ahead on data privacy and security, use compliance automation tools, and team up with regulators to handle these issues.

“At Innowise, we understand that implementing data analytics can be a game-changer, but it also comes with its own set of challenges, especially for banks just starting out. No need to stress — we’ve got you covered. Our team is with you every step of the way, from the initial conversation to the final setup, making sure everything runs smoothly and stays budget-friendly.”

Siarhei Sukhadolski

FinTech Expert at Innowise

The use of data analytics in banking: real cases

Austin Capital Bank had a tough time using data effectively, with their top-down approach limiting access for everyone except the data team. Ian Bass, the new Head of Data Analytics, revamped things by setting up a Snowflake environment and a self-service analytics platform. This change allowed team members across the bank to get insights directly without needing to be tech experts. The result? They cut paid search costs by 50%, boosted revenue margins by around 30%, and improved customer retention by 15% with better marketing insights.

JPMorgan Chase & Co. has refined its risk management with big data analytics and ML. These tools help the bank find fraud indications that human analysts might miss. They also use predictive analytics to spot potential future risks and act before problems arise. New simulation models let JPMorgan see how different market situations could affect its portfolio and make stress testing more accurate. This has led to fewer fraud losses and better financial health.

Deutsche Bank was struggling with market manipulation detection because they had to copy data across different systems. To solve this, they turned to Google Cloud’s BigQuery and Dataproc. Now, data flows directly into BigQuery, making it easier to monitor trades without the hassle of copying. Cloud Composer takes care of the data processes, improving data quality and cutting data transfer costs. By using a pay-as-you-go approach, they’ve saved up to 30% on IT costs and improved their risk management and response speed.

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Future of data analytics in the banking industry

As competition heats up, data analytics is becoming a key differentiator. Banks are building smarter, data-driven services, and it’s no longer just about having data — it’s about staying ahead of the curve with it. In a nutshell, data analytics is on track for big growth, and it’s only going to get more innovative from here.

Feature	Description	Benefit	Future impact
AI-driven decision-making	Using AI algorithms to improve decision-making processes	Better accuracy and increased operational efficiency	Advanced AI models for autonomous banking
Tailored customer experience	Providing more personalized and interactive banking services	Increased customer satisfaction and loyalty	Predicting customer needs and providing customized products and services
Blockchain and data security	Using blockchain for secure and transparent data management	Improved data security and reduced fraud	Integrity and confidentiality of financial transactions
Open banking	Sharing financial data with third-party providers through APIs	Innovation and a wider range of options for customers	More seamless and integrated services
Regulatory Technology (RegTech)	Using technology to streamline regulatory compliance	Reduced administrative burden and minimized risks	Automated compliance checks and reporting
Global expansion	Expanding banking services into new markets	Increased market reach and revenue opportunities	Insights into local market trends, dynamics, and customer behaviors
Human-centric design	Designing banking solutions with a focus on user experience	More intuitive and user-friendly online banking experiences	Understanding human behaviors, patterns, service preferences, and needs

Wrapping up

Using data analytics is a game changer for banks, whether it’s attracting new customers, improving services, or cutting down on fraud. But here’s the thing: if you want to get real value out of it, you can’t just focus on one part of the process. It’s got to cover all the bases. Integrating it can be tricky, no doubt – but with the right experts by your side, walking you through it, and pointing out where you can improve, it doesn’t have to be a headache. In fact, it’ll help you get the most out of it without all the confusion.

FAQs

How does data analytics help make banking more secure?

Data analytics is a big help for banking security. By using algorithms to sift through thousands of transactions, it can spot anything out of the ordinary—like suspicious patterns or activity—and flag it right away. This means banks can catch and deal with potential threats in real time.

What challenges do banks face in implementing data analytics?

When it comes to data analytics, banks need to keep customer info safe from breaches, make sure the data is accurate with regular checks, and deal with the high costs of tech. On top of all that, they’ve got to navigate tricky data protection laws, which makes things even harder.

How can data analytics improve operational efficiency in banks?

Data analytics helps banks make better use of their resources and smooth out their processes. It also highlights where things are slowing down, automates routine tasks, and even predicts when maintenance is needed to keep everything running like clockwork.

What role does data analytics play in risk management?

Data analytics helps banks catch fraud and suspicious activity in real time by analyzing transactions. It also uses past data to predict future risks and prepares banks for different scenarios to keep them ahead of potential problems.

Can data analytics help banks increase their revenue?

Yes, data analytics can definitely help banks increase revenue. It helps them understand what customers want so they can offer personalized products and services and keep customers coming back, fine-tune pricing, and spot new market trends to drive revenue in the long run.

Siarhei Sukhadolski FinTech Expert

Date: Oct 24, 2024

Siarhei Sukhadolski FinTech Expert

Date: Oct 24, 2024

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