Big Data in Banking: Use Cases, Challenges and Trends

Running a bank means being under constant pressure from various directions. From the regulators’ side, you must deal with increasingly stringent reporting requirements and growing compliance demands. Moreover, the banking industry is becoming increasingly competitive, and customers have exceedingly high expectations for the security, speed, quality, and diversity of services. Implementing big data analytics solutions can help banks rise to these challenges, but it also requires comprehensive modernization.

Below, we will discuss how big data can benefit financial institutions today and provide guidance on implementing it effectively. The results of this technology integration should help your banking business facilitate compliance, improve operational efficiency, enhance customer experiences, and become more competitive in terms of service speed, reliability, and sustainability.

Why Big Data Is Important in Banking

The banking industry can benefit from the use of big data analytics because it can unlock valuable real-time insights from the immense volume of information it generates. Financial institutions collect data on transactions, interactions, compliance reports, and other relevant information. All of this can be used to better understand global cash flow, gain insight into market and economic operations, and target customers more effectively.

Some of the business outcomes unlocked by using big data in banking include:

• Personalized customer experiences that improve retention and reduce churn.
• Fraud detection and risk management accuracy increase, resulting in fewer false positives and a reduction in wasted resources.
• Predictive insights that accelerate decision-making and allow implementing proactive strategies.
• Automated governance frameworks that strengthen compliance with GDPR, PSD2, AML, Basel III, and other regulatory mandates.
• Cloud-enabled scalability can reduce infrastructure costs and support long-term growth in the digital banking industry.

Key Challenges and Concerns with Big Data in Banking

Implementing big data in banking can be challenging on many levels, including some technological, operational, and governance concerns. The list below outlines both the main issues and solutions that will enable banks to maximize the value of analytics.

1. Data silos and legacy IT systems
   The majority of banks and other financial institutions heavily rely on legacy systems that are both difficult and expensive to replace. Moreover, banks often run on fragmented platforms that manage retail, corporate, risk, and compliance lines of business. Therefore, the overall data quality is relatively poor due to inconsistency, incompleteness, and duplicates. This complicates analytics and audits, making it hard to use big data in banking.
   The solution involves the gradual modernization of legacy systems using unified big data lakes or warehouses, along with the enforcement of strict governance practices.
2. Compliance pressure and regulatory complexity
   Banks must comply with multiple legal regulations, including GDPR, CCPA, Basel III/IV, PSD2, and AML. These rules govern how financial service providers must store, monitor, and report data. Implementing AI to facilitate and automate big data management is also under scrutiny today due to limited model explainability.
   To address this challenge, banks must embed compliance into the very architecture of their systems and big data analytics tools. They must use automated reporting pipelines, governance frameworks, and transparent model documentation for AI and ML solutions.
3. Data security and privacy risks
   Customer and transactional information is highly sensitive, especially when concentrated in centralized platforms. Therefore, it makes an attractive target for both external and internal attacks.
   The best way to mitigate these risks in big data in banking and analytics projects is to create a multi-layered security strategy that covers encryption, zero-trust access controls, privacy-by-design frameworks, enforced consent management, and data minimization across analytics processes.
4. High costs of data infrastructure
   Big data is expensive in many ways due to vast volumes of information in different formats. Banks often have to deal with petabytes of both structured and unstructured data in real-time pipelines. This requires costly storage and multiple additional expenses.
   The best solution for cost management is to adopt cloud architecture on a usage-based pricing model and implement automated lifecycle management. Additionally, banks should provide support with FinOps practices that align infrastructure spending with business value and measurable ROI.
5. Model risk and explainability
   As many AML (Anti-Money Laundering), credit risk, and fraud systems used by banks today are enhanced with AI and ML features, model explainability and related risks are a significant concern. This is especially relevant when working with big data access.
   To prevent issues related to AI, banks are recommended to adopt explainability frameworks and full audit trails that document assumptions, inputs, and outputs. Additionally, it’s imperative to conduct regular model fairness validations and ensure that it complies with evolving legal regulations being developed worldwide.
6. Real-time processing demands
   Working with big data in banking requires tremendous processing power, which is a technical, operational, and budgeting challenge. Analytics relevant for banks, such as personalization, are based on millions of events per second.
   In order to ensure that such systems run smoothly and provide relevant insights, they must be based on a reliable architecture with standardized ingestion pipelines that can process diverse data formats and quantities. It’s best to use real-time analytics with adaptive thresholding techniques to improve accuracy and reduce false positives.

Big Data Analytics Use Cases in Banking

Rising expectations from customers and regulators force banks to innovate and upgrade legacy systems, implementing cutting-edge tech to stay relevant and competitive. Real-life big data use cases in banking demonstrate how forward-thinking organizations benefit from these solutions across regulatory, operational, and budgetary fronts.

Fraud Detection and AML Monitoring

Big Data analytics is a crucial element of fraud prevention and AML compliance for banks now. Due to the massive volume of transactions, manual processing for anomalies would take too much time. Therefore, banks use Machine Learning to monitor all these events and flag any suspicious activity in real-time. One of the primary benefits of such modern systems is that they can adapt and evolve in response to changing fraud tactics. They also notice even the subtlest anomalies, therefore, reducing overall risks.

Another essential advantage of using big data in banking and fraud detection is the reduction of false positives and alert fatigue. Such false alarms are a common issue for rule-based legacy systems, which overtax investigative resources.

A great example of implementing big data in fraud prevention and AML is Fraud.net. It relies on cloud-native AWS solutions to pull anonymized transactional data from multiple institutions. This technique resulted in a 66% reduction in fraud incidents across the network with 99.9% accuracy in risk scores assessment. This case demonstrates that analytics can be an integral part of proactive defense, meeting operational and regulatory demands.

Credit Risk Assessment and Underwriting

Implementing big data analytics solutions is completely changing the way banks can perform risk evaluation and make underwriting decisions. It provides access to a wealth of information that extends beyond traditional credit score databases, including transaction histories, savings patterns, and shopping behavior. With a better understanding of the potential borrower, banks can evaluate their creditworthiness more accurately. Therefore, they can make less risky financing decisions much faster.

Société Générale uses this technology and big data to its fullest extent. It migrated the entirety of its risk calculation engine to AWS. The bank deployed a new risk computation platform and successfully reduced operational costs for risk calculations by a factor of seven. All of it is due to the scaling capability of their new platform, which has access to thousands of compute nodes on demand. Moreover, the development cycles for new models got 20% faster. Additionally, the bank obtained the agility to run much quicker simulations in stress situations and was able to benefit from it during the COVID-19 crisis.

Regulatory Reporting and Compliance Automation

Among the main reasons why a bank’s strategy for using big data must be exceptionally well thought-out and executed are compliance obligations. The banking industry is one of the most regulated in the world, so financial services face intense scrutiny in their big data use, procurement, and storage. To get an idea of the magnitude of this issue, consider that between 2009 and 2017, various banks were fined over $340 billion for compliance failures and misconduct. Reporting and monitoring are the foremost reasons for these penalties.

Using big data analytics solutions helps banks stay compliant with multiple regulations. One of the greatest advantages is centralization, which streamlines reporting. Automation and routine checks are invaluable tools to maintain compliance. Another major advantage of implementing data analytics is reducing the cost of manual audits and oversight.

A good example of using big data in banking and analytics to provide validation at scale is the US Financial Industry Regulatory Authority (FINRA). This organization oversees securities markets. To that end, it created one of the largest analytics platforms in the world and can ingest big data from over 100 billion banking events daily. The platform runs half a trillion validation checks and allows regulators to detect suspicious activity, such as insider trading or market manipulation, in real-time. FINRA’s choice to rely on cloud-native solutions and environment allowed it to improve query performance by 400 times. Moreover, the organization also reduced its operational overhead and infrastructure costs as a result of this move.

Operational Efficiency Increase

Implementing analytics solutions powered by big data enables banks to optimize their operational efficiency. Utilizing legacy systems in such sprawling operations, which must process thousands of events per day, is largely inefficient. It’s because complex, inflexible architectures make the processes slow and sometimes unreliable. For a bank, this results in delays and monetary losses due to high operational costs and risk of errors.

Consider Mitsubishi UFJ Financial Group (MUFG) of Japan to see how a big data analytics-powered transformation resolves these issues. The organization adopted a cloud-first strategy and began by migrating from on-premises data centers. This immediately cut the infrastructure costs by 20%. Next, they implemented automation using ML models to manage routine account transfer requests, which boosted productivity by 30%. Consolidating big data from silos enabled MUFG to utilize predictive analytics models, delivering a high level of personalization to clients. Moreover, implementing analytics dashboards also helped them reduce report aggregation times and reporting costs by 25% and 70%, respectively.

How Regulations Shape Big Data in Banking Across Regions

Due to the differences in policies, the regulatory frameworks that govern banks are country-specific. Below, we explain how big data analytics is implemented across financial organizations in the US, Europe, and Latin America. We will cover governance standards and practical methods used by banks to meet them. Additionally, we will discuss systemic forces that accelerate or slow down the progress in analytics adoption.

United States: Breaking Silos amid Regulatory Pressure

The US has stringent regulations related to financial services and the use of big data. These organizations also commonly suffer from utilizing aging technology, yet being unable to fully upgrade. This leads to fragmented data silos, which complicate audits and generally slow down operations. Moreover, such legacy systems are expensive to maintain. These costs also grow progressively as it becomes increasingly challenging to synchronize legacy components with advancing technological innovations.

US regulators that monitor bank compliance raise their expectations regarding both big data governance and risk management. They demand that the systems used by financial institutions are capable of withstanding contemporary threats, which requires overall modernization. Weak data controls carry heavy penalties, as you can see from the $250 million fine recently paid by JPMorgan Chase and a $75 million fine against Citibank.

To avoid such punishment from regulators, banks are increasingly turning to compliance testing services. They allow the organization to validate controls and ensure compliance. The service also helps streamline audit preparation, which demonstrates regulatory readiness.

Moving to cloud platforms is an efficient solution for US banks that face regulatory pressure to modernize. This change opens access to real-time analytics tools, big data, and enterprise-wide data lakes. To make this move successful, the organization must invest in agile architectures that meet regulatory requirements and incorporate advanced features, such as real-time fraud detection, personalized UX, and streamlined reporting.

Europe: Data Governance Under GDPR and PSD2

Big data strategies in Europe differ significantly due to the focus on privacy laws. GDPR regulates the use of personal data in the EU. Therefore, all banks must ensure not only maximum protection of this sensitive information but also maintain transparency in their handling of it. Meanwhile, the exchange of this data with third-party providers is regulated by PSD2 and requires the customer’s explicit consent.

Considering these requirements, financial service providers operating in Europe must prioritize big data governance. When working with big data analytics, banks require a secure infrastructure that seamlessly shares information through APIs, while maintaining privacy and adhering to consent norms. They also need to utilize encryption, anonymization, and the ‘right to be forgotten’.

It can be challenging to balance all necessary legal requirements within a single infrastructure, as GDPR emphasizes data minimization and protection, while PSD2 calls for transparency and portability. The solution used by many EU banks is to enforce internal big data controls and build unified data lakes. They adopt enterprise data catalogs, lineage tracking, and detailed audit trails to promote visibility as required by regulators.

Latin America: Fintech Disruption and the Inclusion Gap

The disparity between rapidly evolving fintech businesses and outdated legacy systems shapes the implementation of big data analytics in banking across Latin America. This region has one of the world’s most dynamic fintech ecosystems. However, over half of the residents in LATAM are underbanked or even unbanked.

Traditional financial institutions run almost exclusively on legacy infrastructure. Therefore, incorporating big data in any way is challenging and expensive. Banks struggle to deliver personalized services or perform real-time analytics due to data silos. As a result, they are unable to compete with less-regulated fintechs that use big data analytics and other tools to deliver online banking services faster and with less hassle.

Regulators in the key markets, such as Mexico and Brazil, are pushing to enforce secure big data sharing via APIs and turn toward the more open banking industry. This helps accelerate modernization in traditional banks and forces migration to the cloud. However, the process is slow, and the implementation of big data is not yet widespread. Currently, banks are beginning to utilize alternative data sources, including mobile phone usage and e-commerce transactions, in conjunction with AI-powered analytics. Unfortunately, there won’t be significant overall progress in the banking industry without overhauling outdated IT systems.

How Romexsoft Can Help Banks Harness Big Data

Implementing big data within a bank requires a comprehensive approach that encompasses technological upgrades, an agile cloud-first architecture, and a robust security strategy, all of which are connected within a cloud-based infrastructure. Moreover, this whole system must also comply with relevant legal regulations and feature advanced analytics. Romexsoft has experience in assisting banks and other financial businesses in transitioning to such infrastructure on AWS-based big data platforms custom-tailored to the FinTech sector.

Our approach to developing such big data solutions covers:

• Unified Data Foundation
  We can consolidate fragmented banking industry data into centralized warehouses and data lakes on AWS. This allows your technical and analytical teams reliable access to all information, enabling consistent observation. Centralization and visibility are essential for compliance when working with big data in finance.
• Elastic and Cost-Optimized Infrastructure
  AWS offers elastic computing and storage, which are essential for efficient cost management. We design systems that maximize the cost-value ratio while adjusting as your business scales with demand. Such flexibility enables banks to utilize big data and AI agents to run complex simulations without incurring permanent infrastructure overhead.
• Real-Time Analytics
  When using big data, you need real-time analytics to draw maximum benefits from it. We create serverless pipelines and streaming architecture that allows near-instant access to intelligence and changes within. This enables banks to transition from slow, batch-based processing to real-time decision-making. This feature is also crucial for more accurate risk scoring, effective fraud detection, and personalized services.
• Embedded Compliance and Security
  Architectures we create incorporate security elements such as encryption, data lineage, retention policies, and comprehensive audit trails. Therefore, it’s easier for banks to establish and maintain compliance with GDPR, PSD2, AML, and Basel III frameworks.
• Operational Automation
  Our experts can incorporate automated ETL, monitoring, and testing workflows that reduce errors and cut costs. This reduces manual labor and lowers risks to the bank while freeing up employees’ time spent on repetitive routine tasks.

Romexsoft has expertise in both AWS and fintech solutions and can become a trusted partner to banks interested in a big data ecosystem. We can provide a scalable, compliant, and secure infrastructure tailored to the business’s unique needs.