The evolution of the banking sector in recent years has seen several important trends, including data science. The shifts in the expanding market environment, technology stack, and the general outlook have shaped the necessity for big data analysis. This cutting-edge approach has significantly helped banks to refine and modernize the way they operate while reaching new milestones in client service.

Statistically speaking, the financial analytics market is experiencing a steady rise. It is projected to reach an impressive 22.09B by 2032, showing a CAGR of 11.57%. Artificial intelligence, dynamic and predictive analytics empower financial entities, promoting informed strategic choices. Naturally, the banking industry doesn’t neglect these trends, yielding notable results.

The current article will review the core positive impacts and challenges that are present in banking analytics, emphasizing its practical aspects.

What is banking analytics?

The digitalization in finance has put forward many innovative technologies and procedures. Undoubtedly, one of them is big data analytics in banking. It stands for the examination, conversion, and modeling of basic data to promote strategic thinking and decision-making. The helpful insights gained allow financial brands to remain relevant in the ever-changing global market.

The basic data for banking analytics comprises client transactions, activities, interactions, and diverse touchpoints within the ecosystem. The ultimate aim is to devise and implement an effective operating strategy, namely

• strengthen and expand client service,
• perform risk management,
• modernize operations and boost efficiency,
• ensure banking regulations, and
• maintain a strong competitive edge

Banking analytics assists finance entities in steering the dynamic market conditions and moving forward. The number of U.S. online banking users will be reaching around 216.8M in 2025. This illustrates that digitalization, along with data analysis, plays a key role in a bank’s thriving future.

Read also: Big Data in the Banking Industry: The Main Challenges and Use Cases

5 Benefits of utilizing data analytics in banking

Financial analysis is, undoubtedly, a useful asset to any banking brand, offering many positive outcomes. The better grasp of clients’ priorities and actions enables highly personalized financial services. This, in turn, enriches the general experience while building and sustaining a loyal client base.

Let’s examine the main benefits of data analytics in contemporary banking:

1. In-depth client profiling

The crucial factor for client retention is anticipating and meeting their demands with a financial product. Consequently, a successful brand must implement adequate banking customer analytics. This will help separate the facts from assumptions, providing clients with tailored offers, such as investments or loans. Customer base segmentation is crucial to improving sales and marketing efforts for the brand.

One way to get a grasp on client preferences is sentiment analysis. It represents contextual text mining, extracting subjective data that reveals the social sentiment of a brand. The textual data is obtained via reviews, online networking comments, and other communication channels. These sentiments can help modern banks pinpoint the strong and weak elements of client satisfaction policies.

Using big data in profiling is set to boost retention rates. Millennial/Gen Z clients prefer banks to be their companion and consultant in monetary matters. Greater customization of services combined with tailored marketing produces a winning scenario for a banking brand.

2. Improved risk management

The client pool of a standard bank is inherently diverse, which brings on certain impediments for the institution. Credit risk and prevention of fraud remain the crucial elements of financial safety. Data processing unlocks many possibilities to successfully alleviate and manage the upcoming risks.

Let’s take fraud analytics in banking as an example. By evaluating client activity patterns, the security system can detect suspicious incidents, such as unauthorized login efforts. If a login geolocation doesn’t match the typical pattern for a client, they will get immediate notifications. Likewise, any deviations from a historical transaction pattern can be flagged as suspicious.

The new data analysis technologies enhance the bank’s ability to identify and prevent fraudulent actions. As a result, customers are more likely to trust such financial brands.

3. Operating cost savings

One thing the automated data analytics for banks excels at is cutting down the pricey and outdated manual procedures. New tech stack, namely artificial intelligence (AI), can greatly improve the allocation of assets while unloading the staff. Even more intricate tasks, for example, boosting the efficacy of contact centers, can derive benefit from such tech.

As mentioned earlier, data analytics is a useful means of determining strengths and weaknesses within systems. Unnecessary expenses and unproductive tactics grow more outlined when expressed in actual numbers. Combined prescriptive and predictive analytics in banking opens a path to more refined but less costly strategic planning.

Utilizing data analytics with proper preparation allows banks to attain optimized and effective processes. This, in turn, efficiently improves the general performance.

4. Regulatory compliance

Given the large stock of data deployed in modern banking, keeping pace with industry regulations is becoming complicated. That is exactly where data analytics is able to make a real difference, assisting banks in pinpointing possible issues and avoiding fines. The bank’s reputation is as relevant as ever amidst ardent competition. Preserving it with new technologies is much easier and more efficient.

Analytics tools can monitor activities and transactions as it happens, detecting any deviations from compliance standards. Unusual patterns of conduct and potential fraudulent actions can be spotted and handled with extreme speed and precision. Such instruments are also valuable for automated compliance reporting, decreasing the chance of error.

Utilizing data analytics to secure compliance helps banks adapt to the dynamic framework. Fewer fines and issues will result in an elevated reputation for the financial establishment.

5. Development potential

Just like any other business, a bank must evolve to maintain its market position. With all the advantages of innovative big data analytics, it can outpace the more conventional competitors. Fine-tuned sales campaigns, enhanced recommendations and personalized offers create ample room for further expansion and development.

One way to boost the margin is by identifying high-value individuals within the client base. Targeting this segment will boost the performance of marketing initiatives and resource distribution. In the long term, such a strategy will enhance client relationship management, fostering brand commitment.

Data analytics practices directly influence the cross-selling and up-selling potential, cost optimization and market expansion. Seizing these opportunities can greatly advance revenue generation and total growth.

Banking data analytics: Use cases

Big data is actively supporting innovation and greatly enhancing client experiences within the sector. Many banks have adopted data analysis, obtaining useful observations of customer activities.

Let’s dive into 5 banking analytics use cases and the ensuing advantages.

Credit modeling

A financial risk analytics model would likely entail income sources, loan record, and credit rating. Big data enables banks to throw in even more variables for a more precise analysis. These may incorporate monthly spending, utility bills, online presence, etc. Such insights into a client’s financial wellbeing showcase how analytics and AI will reshape banking.

Implementing data analytics in the banking industry can be immensely beneficial. Precise credit rating plus risk evaluation present a path to decreasing delinquency rates as well as improved loan portfolios.

Around 2.36% of all consumer loans in the USA were delinquent during the 2nd quarter of 2023. Confronted with these figures, commercial banks would welcome an advanced and data-centric risk evaluation strategy.

Customer segmentation

Tailored marketing efforts become much more efficient with using the correct segmentation of the client pool. Separating customers depending on specific characteristics, actions, and priorities is critical for fulfilling their requirements. For banks aiming to stay relevant, a data-driven approach is among the top choices.

Let’s take credit rating and loan offers for instance. By evaluating credit scores together with back data, banks can customize loans, credit limits, and rates for segmented clients. Such aspects as debt-to-income ratio, timely payment of bills and loans, credit mix, etc., can influence the outcome.

Customer analytics in banking also opens up the possibility to develop segmented marketing campaigns while improving expectation management.

With 72% of bank account holders seeing personalization as very relevant, objective customer segmentation is crucial for achieving success.

Churn prevention

When a client stops using a service or product, the banking establishment takes a loss. Improving retention rates is attainable via data analytics and targeted loyalty programs. The more precise a client profile, the higher the chances of preventing attrition.

By scrutinizing such components as transaction rate and type, payment record, etc., banks can create a relevant client profile. For example, consistently low transaction activity could be an indicator of a potential churn. The same could be true in the case of a sudden decrease in a client’s balances. Personalized communication, tailored services, or a loyalty program can help deal with the issue.

Numerous studies have demonstrated that acquiring a new customer could cost up to 5 times more than retention efforts. Using this knowledge, institutions can back up advanced analytics in the banking industry for developing relevant offers.

Stress testing

Mitigating risks is always more rewarding than addressing unexpected shocks and losses in a financial system. Capital adequacy, liquidity, and portfolio strength can be tested with big data analytics in the banking industry. It can also be utilized for determining how well a bank can withstand unexpected shocks.

Stress tests are employed for simulating various scenarios (higher loan rates, rapid alterations, adverse environment, etc.). Their aim is to determine if the existing resources are sufficient to overcome the problems. If there are gaps, banks have the option to make the required adjustments for minimizing possible risks. Ultimately, such analytics use cases in banking will prevent economic damage, upgrading risk management systems.

External stress testing is a standard practice for the U.S. as well as European banks; however, any institution can conduct it in-house. The key component to achieving more precise – and valuable – outcomes lies in efficient data analytics for banks.

AI virtual assistants

Considering the ongoing shift towards self-service, banking clients are shifting to choosing it over in-person contacts. Answering basic questions about financial services and helping with less complex issues is where AI assistants are most successful. They can also validate certain criteria that make clients eligible for services or offers.

What makes AI assistants indispensable in banking is the improved allocation of resources. Instead of sorting out small issues, the managers and support operators can delegate such tasks to AI. This enables a focus on the crucial and complex problems to boost operational efficiency. Concurrently, interactions with modern AI assistants can feel sufficiently human, preventing client loss.

Integration of AI assistants into banking creates a more productive ecosystem, yielding benefits for both institutions and their clients. After all, conversational AI tech may reduce contact center labor costs by a staggering $80B by 2026.

The challenges of introducing big data analytics into the banking industry

Despite a lot of advantages, incorporating big data into banking comes along with several challenges. They can manifest in data quality, safety concerns, banking regulations, and other obstacles.

The key big data implementation challenges in banking are as follows:

Regulatory compliance

The financial sphere is strictly regulated, meaning that data analytics solutions for banking must conform to the guidelines at all times. The regulations can be quite intricate, but compliance is imperative for preserving a bank’s integrity.

The instances of mandatory banking regulations are Know-Your-Customer (KYC) and Anti-Money Laundering (AML). KYC involves the steps to authenticate the identity of clients and maintain updated information for eliminating fraud. AML covers proactive measures against money laundering. There is also data management standards that must be followed to secure full compliance and minimize legal risks.

Data security and confidentiality

Banks frequently use highly sensitive data, such as private financial information. Big data analytics has to sustain a strict focus on overall data safety to uphold the trust of the clients.

Data protection regulations, including GDPR and its local equivalents, are vital in the banking sector. Financial regulations often include specific requirements for data administration and safekeeping.

By following these rules and standards, banks can significantly reinforce data safety and user privacy. Balancing innovation with security is the right way to approach such challenges.

The intricacy and quality of data

Banking data exists in varied forms, and it is feasible to derive it from a diverse range of channels. Client interactions, market details, even social media inputs can be utilized in banking predictive analytics. Controlling this complexity may prove to be a challenging task.

Different data sources, its volume and variety call for advanced extracting, evaluating, and structuring techniques. If a bank works in a complex interconnected environment, securing data flow and consistency becomes crucial.

Data confidentiality and safety must also be observed to prevent breaches or unauthorized access. With so many factors and variables, a strategic approach to the operation of data is imperative for banks.

Risk prevention

As mentioned earlier, banking institutions may face an array of risks, comprising of market and credit, as well as operational hazards. Since the risk landscape is always dynamic, data analytics in the banking sector can cause some challenges in this aspect.

Complex, voluminous data flow can overwhelm traditional risk management systems. If the information is incorrect or partial, this can cause flawed evaluations. Regulatory adherence may also be difficult, requiring system updates and continuous checking of data. Productive risk prevention depends on the bank’s flexibility and willingness to finance upgrades.

How to build an effective banking analytics strategy

Building a viable strategy in banking analytics presupposes a comprehensive and systematic approach. The more planning made before greenlighting, the more productive the final strategy can become.

Let’s discover the top practices to follow in this case:

1. Expand your strategy gradually. An all-in-one-go solution may seem effective, but actually it’s often highly unrealistic and costly. Instead of trying to solve every issue a.s.a.p., banks do better with a modest start by testing a few methods. The ones bringing results can be implemented with proper financing and assets.
2. Examine and assess the tech and workplace learning investments. This is an important step for making sure that the selected technologies meet the bank’s evolving needs. The evaluation of tech investments involves assessing the organizational effectiveness, adaptivity, and relevance of analysis tools.
3. Create a data ecosystem with in-house and outside sources. Internal banking big data is undoubtedly valuable; however, external data offers additional context to the findings. For this reason, banks should incorporate external outlets in the analysis whenever possible.
4. Collect customer insights via numerous data sets. Building a full view of clients requires many data sets and sources. Customer data platforms facilitate the accumulation of relevant data through every touchpoint and enable easy sharing.
5. Adopt the top efficient solutions. Any strategies, techniques, or policies that don’t deliver should be set aside, however familiar or convenient. In this way, banks can cultivate a truly adaptive and dynamic analysis method, keeping ahead of the competitors.
6. Look for smart and user-friendly solutions. Streamlined design, adequate onboarding, mobile accessibility, and safety shape up the end-user experience. The use of data analytics in banking is geared towards creating positive impressions, calling for intelligent user-centric systems.
7. Focus on maximum automation. Low-level requests, such as basic account-related actions, can take up a lot of valuable resources. Automating their fulfillment is a big step towards efficient time management, where high-ranking requests receive the top priority.
8. Collaborate with a professional solutions provider. Implementing advanced analytics in banking is hardly feasible without a diverse team of experts. The latter should be adept in data science and have ample experience with the financial services industry.

Thank you
for downloading the guide!

Free Guide to Your Project Budgeting

Explore real-life examples and discover the ways to stay
within your budget.

First name * Field is required

Last name * Field is required

Email * Field is required

I consent to receive e-mail communication from Eastern Peak in accordance with Privacy policy.

Free download

Our work example of fintech analytics solutions

Western Union

Our team at Eastern Peak has developed a comprehensive web crawling engine and data mining system for Western Union for various financial information to be collected, normalized and analyzed.

An adaptive and customizable reporting dashboard with extensive data visualization tools that facilitates competitor analysis, report generation and configuration management.

In conclusion: Advanced analytics in banking

Big data analytics is one of the leading trends in today’s banking. It involves a comprehensive analysis, transformation, and modeling of basic data to accumulate valuable information and offer greater insights.

Data analysis supplies banks with invaluable potential for customizing marketing campaigns as well as financial services. Consequently, they are able to develop productive operating strategies, advance risk management, and improve client satisfaction.

The core advantages of using data analytics in banking comprise of the following:

• in-depth customer profiling,
• refined risk management,
• operational expense reduction,
• regulatory compliance, and
• development potential.

Want to invest in big data analytics solutions for your own financial brand? Our team of experts can build a successful operating strategy based upon data-driven insights and help you outclass the competition. Contact us for more detail – and start with the digital transformation!

Frequently Asked Questions

Read also:

• Big Data in the Banking Industry: The Main Challenges and Use Cases
• Predictive Analytics: Transforming Data into Actionable Insights
• 8 Trends Empowering the Customer Experience in Digital Banking
• Examining AI Uses in Banking & Financial Services