With an estimated 402.74 million terabytes of data generated every day, making sense of that data is the key to securing profitability, resilience, and sustainable growth of your financial business.
The statistics prove the importance of leveraging data analytics for finance — those organizations that don’t leverage data for financial services personalization lose more than 50% of customers. On the other hand, those leveraging the power data can expect significant advantages for their finance business, such as 9% annual portfolio growth and insightful investment ideas with enhanced alpha potential, as 98% of investment professionals agree.
Implementing data analytics in finance is an excellent move for your organization. However, it requires an expertise-driven approach to breaking silos, addressing technical debt, and ensuring compliance and security.
The Impact and Evolution of Data Analytics in Finance
Traditionally, data analytics in finance focused on answering two questions: “What happened?” (descriptive analytics) and “Why did it happen?” (diagnostic analytics).
Today’s data analytics evolved far beyond those traditional approaches. It is combined with AI/ML that is fueled by petabytes of customer, market, transaction, and other data. The ultimately large data volumes analyzed by sophisticated machine learning algorithms allow for:
- Predictive analytics that forecasts future outcomes and runs simulations to predict market trends and assess risks
- Prescriptive analytics that recommends the next best action for optimization and better decision-making.
The Rise of Predictive Analytics in Finance
As the name suggests, predictive analytics algorithms can determine the likelihood of a specific event based on historical data. In finance, these predictions can concern fraud, credit risk, demand and sales forecasting, and regulatory compliance.
Fraud Prevention
Compared to traditional rule-based methods, credit card fraud detection using machine learning offers significantly enhanced accuracy and adaptability. Predictive analytics, in turn, transforms detection into prevention, enabling ML models to catch fraudulent behavior patterns before the risk is realized. This way, financial institutions can anticipate potential threats and proactively block suspicious activities, at no cost to customer experience.
Credit Risk Management
Predictive analytics can predict how likely a consumer is to default on a loan, enabling a more granular risk assessment in underwriting. Combined with alternative data, it becomes even more potent. Organizations can take into account hundreds of additional data points, such as transaction, telecom, utility, rental, social media activity, clickstream, and survey and questionnaire data.
Managing Demand Equation
Predictive analytics can forecast consumer behavior and balance the supply and demand for financial products and services. This, in turn, helps maintain liquidity and maximize capital efficiency. For example, it can predict that more consumers may start applying for loans following a decrease in federal interest rates.
Dmytro Timofiev
Delivery Manager at SPD Technology
“Predictive analytics in finance enable organizations to improve resilience and operational efficiency. Combined with prescriptive analytics, predictive models can become a valuable asset from front- to back-office.”
Predictive Sales Analytics
Using historical and real-time data, predictive models can forecast sales volumes, cash flows, and revenues. This helps ensure compliance with liquidity requirements and maximize capital efficiency. Furthermore, predictive models can be used for more effective lead scoring, targeted promotions and marketing campaigns, and upselling and cross-selling recommendations.
Predicting Possible Compliance Issues
Predictive models can identify and prioritize compliance risks. They can do so by powering regulatory horizon scanning and intelligence across hundreds of jurisdictions. They can also reveal emerging regulatory trends, inform prescriptive analytics for mitigating compliance risks, and simulate various regulatory scenarios.
Strategic Advantages of Using Financial Data Analytics
In 2025, 45% of financial organizations worldwide are expected to adopt AI for data analysis, considering this technology critical for gaining a competitive advantage. Below are more strategic benefits of leveraging data analytics capabilities for the finance sector.
Enhanced Risk Management
More accurate risk and mitigation strategy assessment is one of the principal advantages of introducing data analytics in finance. In fact, risk management is the leading AI and data analytics use case among financial organizations, with 36% reporting this use case for the two technologies.
As climate and cybersecurity risks pose novel challenges to risk management strategies, predictive models can offer better visibility into the likelihood of risk realization. These models can also ingest hundreds of data points in real time to run simulations and assess the consequences of various realized risks.
For example, insurers can train predictive models to assess the likelihood of extreme weather events during P&C policy underwriting to inform pricing. Accurate risk assessments will then ensure liquidity and solvency if those risks are realized. All in all, leveraging advanced data analytics allows you to enhance risk management, improve the efficacy of your mitigation strategy, and quickly assess detect and novel threats.
Data-Driven Business Strategy Development
Truly data-driven strategic decision-making is another advantage of data analytics in the finance industry.Three-quarters of CEOs already rely on operational and transactional data to make decisions. However, 82% of companies continue to make decisions using stale data.
With the volumes of data available rising exponentially, making sense of that data can help you take advantage of a new business opportunity, improve operations, and make smarter investments.
For example, combined with data visualization, financial data analytics tools give decision-makers a 360-degree view of every aspect of operations. Data analytics tools can also make the next best action suggestions, thus enabling smarter decisions across the value chain.
Improved Customer Retention and Loyalty
Over half of financial services customers are ready to change providers if provided services come across as impersonal. So, CX personalization isn’t just a trendy objective—it means meeting customer expectations.
For example, Bank of America’s chatbot, Erica, leverages data analytics to provide proactive financial advice to its users. Predictive analytics can also suggest the best next action for preventing customer churn and cross-selling or upselling.
Like data analytics in ecommerce, using data analytics to streamline back-office processes and personalize CX translates into higher customer retention, loyalty, and lifetime value.
Dmytro Tymofiev
Delivery Manager at SPD Technology
“Treating every customer as a segment of one is impossible without advanced data analytics. However, to implement it, assess your organization’s data maturity first to ensure the analytics tools rely on accurate, up-to-date data.”
Reduced Operational Costs
Data analytics can also help you create operational efficiencies, decreasing running costs as a result. According to NVIDIA’s survey, 35% of U.S.-based financial organizations leveraged AI and data analytics for this exact purpose, while 20% more used the technology to reduce the TCO. A third of them managed to reduce costs by over 10%.
As galloping inflation since 2020 has substantially decreased consumers’ purchasing power, competing on price has become more crucial than ever. Cutting operational costs enables you to offer products and services at more affordable prices.
For example, data analytics can identify and eliminate inefficiencies and bottlenecks across functions. It can also automate fully or partially certain tasks, such as risk assessment, investment portfolio optimization, or KYC checks (e.g., perpetual KYC). Besides enabling you to compete on price, reducing operational costs is also key to maintaining and improving profitability in turbulent market conditions.
First-Mover Advantage
Finally, leveraging the business impact of Big Data and advanced data analytics ahead of competitors allows you to secure the first-mover advantage. Furthermore, 75% of CEOs believe that competitive advantage will hinge on who has the most advanced generative AI – and genAI requires high volumes of quality data to be trained on.
Advanced analytics capabilities allow organizations to offer cutting-edge new products and services. For example, organizations already leverage AI and data analytics to offer their customers products like parametric insurance (e.g., Descartes Underwriting) or access to high-growth stock indexes (e.g., HSBC). Securing the first-mover advantage means strengthening your brand recognition, boosting customer loyalty, and gaining a higher market share and share of wallet.
Main Use Cases of Data Analytics in Finance Verticals
As finance and data analytics go hand in hand, offering a wide range of use cases that drive strategic growth. Here’s how data analytics transforms operations across five verticals.
Data Analytics in Banking
Corporate and commercial banks are already leveraging advanced analytics for fraud detection and prevention, CX personalization, and credit risk modeling. However, the list of potential data analytics use cases in banking doesn’t stop there. Data analytics can also power:
- Predictive maintenance for ATMs and other equipment
- Intelligent support ticket triaging
- Response suggestions for call center agents
- Automated cybersecurity threat detection and response
- Personal finance management suggestions and tips for customers
- Branch performance and resource allocation optimization
- Cash flow analytics and liquidity forecasting.
Benefits of Data Analytics in Banking
Leveraging data analytics allows commercial and corporate banks to sharpen their competitive edge with:
- Hyper-personalized CX, powered by a 360-degree view of customers and behavioral and sentiment analysis.
- Improved customer lifetime value, thanks to more effective churn prediction and outreach campaigns.
- Higher conversion rates with more accurate lead scoring and tailored product recommendations.
- Granular product pricing with what-if modeling and advanced risk assessment.
- Data-driven decision-making on all levels with business intelligence tools.
- Enhanced risk assessment, prioritization, and management.
Data Analytics for Insurance Companies
Data analytics in insurance enables companies to offer new products, streamline underwriting and claims management, and optimize risk management with:
- Personalized pricing and coverage powered by advanced risk modeling using traditional and alternative data
- Usage-based insurance that calculates premiums based on driving behavior
- Parametric insurance to leverage alternative data like satellite imagery and weather conditions to identify when the insured event occurs
- Automated data verification and validation during claims processing
- Automated first notice of loss (FNOL) via telematics data analytics.
Analytics in Investment Banking
Data analytics is one of the leading applications of AI in investment banking, with predictive financial modeling as one of the most prominent use case. Traditionally, financial modeling was done manually and carried a high risk of human error. Today, data analytics can take it on, easily taking into account hundreds of variables and detecting patterns invisible to the human eye. As a result, investment banks receive more accurate forecasts of market trends and volatility, asset prices and returns, and liquidity and cash flows.
Investment banks and capital markets organizations are now using the technology to:
- Identify stocks with the highest growth potential to increase ROI (e.g., HSBC).
- Monitor market conditions and identify trends, assess volatility, and predict asset prices.
- Power algorithmic and high-frequency trading.
- Determine the optimal hedging strategy based on market conditions.
- Detect market manipulation.
- Predict liquidity risks under extreme market conditions (e.g., the COVID-19 pandemic).
Predictive Analytics in Accounting
Predictive analytics powers an emerging field of financial management: predictive accounting. This type of accounting leverages big-data-driven forecasts to gain visibility into future cash flows, revenue streams, business growth rates, liquidity, solvency, and more.
These forecasts allow companies to maximize capital efficiency while maintaining compliance with regulatory requirements.
Top use cases for AI-powered analytics in corporate finance also include:
- Anomaly and error detection
- Decision-making support
- Percentage-of-completion revenue forecasting
- Cash collection forecasting.
Data Analytics for Financial Services
In financial services, data analytics can power highly personalized financial planning. Financial advisors can use data analytics to run multiple risk modeling scenarios and recommend the best financial strategy that matches the client’s risk appetite and goals. Predictive analytics in financial services can also help tax professionals grasp how new regulations and developments (e.g., the 2025 tax cliff) can impact the client’s bottom line.
Besides the above mentioned, financial service companies use AI and data analytics to streamline the next processes:
- Wealth transfer and management automation
- Beneficiary designation optimization
- Asset protection strategies enhancement.
The Future Role of Big Data Analytics for Financial Services
Big Data is the future of data analytics in finance for two reasons. The first one is that the volumes of generated data continue rising exponentially and are expected to reach 181 zettabytes in 2025. As for the second, the proliferation of open banking regulations (e.g., PSD2) means financial organizations can gain access to even more data while capitalizing on their data streams.
These three trends will shape the future of Big Data analytics in financial services:
- Collaboration and data sharing across ecosystems: Open banking regulations mandate financial institutions to provide access to customer data via secure APIs.
- Innovative financial products and services: For example, predictive analytics can power cutting-edge fintech products for algorithmic trading, financial advice, tax optimization, etc.
- Enhanced strategic value as a competitive differentiator: Those who harness the potential of Big Data analytics will gain an edge with superior customer experiences, more cost-efficient operations, and never-before-seen products and services.
Main Challenges of Finance and Banking Analytics Implementation
At SPD Technology, we’re well-acquainted with the most common fintech applications development challenges. Below are six of the most important ones.
Data Integration and Possible Silos
The importance of data integration is hard to overstate: data silos are the biggest barrier to innovation and hinder digital transformation efforts for 81% of organizations.
Data silos prevent organizations from getting a complete and accurate view of their financial operations. Breaking these silos is crucial to enabling a 360-degree view and ensuring the output accuracy.
How to address it: Assess the data maturity of your organization before kicking off the data analytics project. Ensure your data is consolidated in a centralized data repository and that you have a robust data governance framework and data strategy beforehand, as well.
Real-Time Data Integration
Aggregating and transforming data from multiple sources in real time can be challenging due to data volumes, velocity, latency, and quality requirements. Real-time processing also needs significant computing resources.
However, certain projects, such as real-time risk assessment and fraud software development, are impossible to complete without integrating real-time data streams.
How to address it: Invest in scalable infrastructure and opt for the event-driven approach to architecture to support data analytics solutions. Verify your data quality controls are robust and your tech stack is adapted to stream processing (e.g., Apache Kafka) to enable real-time analytics.
Legacy Systems at High Costs for Their Modernization
The average age of a universal bank’s core applications is 14 years, with the figure rising to 18 years for an average insurer. Decades-old legacy systems, however, prevent organizations from reaping the full cloud computing infrastructure benefits.
Adopting the cloud is a prerequisite to running Big Data analytics and AI/ML models as these technologies require the cloud to function. Migrating to the cloud, however, is a costly and resource-intensive undertaking. Modernizing legacy systems, in turn, is even more expensive and resource-demanding in most cases.
How to address it: Evaluate your legacy systems and existing technical debt against data analytics needs early on. Identify which systems need to be modernized before implementing data analytics and select the right modernization approach (rehost, re-platform, refactor, rearchitect, rebuild, replace). Consider moving away from legacy infrastructure to the cloud in increments.
Possible Shortage of Data Science Talent
Recruiting tech talent is a challenge across all sectors, with a data scientist’s average time-to-fill reaching 60 days. As the need for data scientists grows together with data volumes, finding the right talent to design, develop, implement, and maintain data analytics tools may not be as straightforward as you’d expect.
The lack of relevant data science expertise during data analytics development and implementation can lead to poorly aligned technical requirements, inaccurate output, and underwhelming ROI.
How to address it: When hiring in-house, consider your internal talent pool and promote employees willing to undergo data science training. You can also tap into the global talent supply. If in-house recruitment isn’t a suitable option for you, consider hiring a data analytics implementation partner.
Risk of Non-Compliance
The financial sector is heavily regulated, and your solution may have to comply with hundreds of regulations across multiple jurisdictions. You should be especially careful when implementing KYC standards in financial services as they should remain unbiased and compliant without sacrificing customer experience. You should also pay attention to ensuring data security in fintech applications, from password hygiene enforcement to authorization and authentication.
Failure to comply with privacy, security, and other regulations can lead to fines and damage to your reputation. You should pay extra attention to novel AI regulations aimed at ensuring model fairness and transparency.
In the United States, the Equal Credit Opportunity Act (ECOA) requires ML models to prevent discrimination in lending. The Bank Secrecy Act (BSA), in turn, imposes requirements for ML models used for transaction monitoring to ensure their effectiveness in detecting suspicious activity.
How to address it: Assess all regulatory requirements for the future data analytics solution before finalizing functional and non-functional requirements towards it. To protect sensitive data, prioritize strong encryption methods (e.g., Twofish, Blowfish, AES, RSA) and data tokenization.
Vendor Lock-In vs Complexities of Custom Finance Data Analytics Software Development
Buy vs build is the eternal question. Getting an off-the-shelf solution may easily result in vendor lock-in, where leaving a vendor is too costly or complicated, but the solution no longer meets your needs. On the other hand, the build approach comes with its challenges: project management, considerable overhead, and alignment with business needs are just a few.
Choosing the unsuitable approach to ramping up data analytics capabilities may undermine the solution’s ROI, lead to resource waste, and prevent you from achieving your business objectives with data.
How to address it: Conduct a thorough buy vs build analysis. Consider cost factors, competitive differentiation, scale and complexity, time frames, and data and security requirements during the analysis. Perform discovery interviews to define and prioritize requirements.
Tested and Proven Approaches to Finance and Banking Data Analytics Integration
At SPD Technology, we routinely help our clients overcome these and other challenges when providing data analytics services. Here are our five tried-and-true best practices to maximize the advantages of strategic technology consulting for our clients – and ensure your data analytics efforts pay off.
Establishing a Strong Data Governance Framework
A data governance framework is crucial for maintaining compliance, security, and data quality. When providing data analytics consulting services, we help our clients design a comprehensive framework that outlines:
- Key data governance roles and responsibilities across functions
- Success metrics and KPIs
- Necessary data security measures
- Data ownership and accountability policy
- Risk management practices
- Data quality
- Master data and metadata
- Architecture management practices
Prioritizing Data Quality
The output of data analytics models is only as good as the quality of data fed into it. That’s why ensuring data quality is a top priority in any data analytics project. It involves:
- Establishing data quality standards across the organization.
- Implementing data validation techniques (e.g., format validation, range constraints) and data cleansing tools.
- Monitoring the quality of master data and other critical data assets.
- Conducting regular data quality audits.
Don’t know where to start with data quality?
Learn more about data quality management in our dedicated article!
Adopting a Hybrid Cloud Architecture
The cloud provides the scalability and computing power necessary for handling Big Data analytics. However, strict regulatory requirements may mandate on-premises data storage. That’s why the hybrid cloud is usually more suitable for financial organizations.
Hybrid cloud infrastructure delivers the best of both worlds: the security and compliance of on-premises storage and the scalability and flexibility of the cloud. When helping our customers adopt it, we start by providing a tailored assessment of their existing IT infrastructure to find a reasonable balance between cloud and on-premises environments. During the next steps, our approach balances compliance with scalability. According to it, we suggest integrating sensitive datasets into a secure, compliant on-premises environment, while using cloud resources for real-time analytics processing and Big Data handling.
Combining Predictive and Prescriptive Analytics
While financial predictive analytics can identify trends and predict cash flows or market conditions, combining it with prescriptive analytics is the ultimate game-changer. Prescriptive analytics determine the next best action to enhance risk management, optimize portfolio management, personalize CX, and more.
To combine predictive and prescriptive analytics for financial services firms, our approach involves a strategic integration of advanced data processing, ML models, and decision-support systems. First, we build predictive models that work within vast datasets to predict risk factors and possible outcomes using real-time data analysis. These predictions form the foundation for prescriptive analytics, where we incorporate optimization algorithms and AI-driven decision engines to suggest the most effective actions in response to those predictions.
Implementing Advanced Risk Analytics and Stress Testing
Advanced risk analytics in banking and financial services allows organizations to mitigate risks effectively and cost-efficiently. Stress testing portfolios, for example, let organizations maximize their preparedness for potential financial shocks.
We help our clients implement:
- Value at risk (VaR) models for quantifying the risk of loss based on market volatility and exposure
- Scenario-based stress testing for assessing resilience and refining risk mitigation strategies based on the most likely outcomes of scenarios (e.g., regulatory changes)
- Liquidity risk monitoring for monitoring and forecasting liquidity levels and requirements to avoid cash flow crises and maintain compliance.
Our Professional Vision of Finance Data Analysis Implementation
As one of the best fintech development companies in London, UK, we specialize in delivering complex data analytics solutions to mid-sized and large financial organizations. Our AI/ML and data science expertise coupled with business acumen enable us to deliver solutions that become the value-driving backbone of enterprise decision-making.
Our approach to data analytics projects rests on three pillars:
- Building a scalable and flexible architecture that evolves together with the business
- Helping break down data and organizational silos
- Emphasizing the use of predictive analytics.
See our expertise in action in our case study on real-time data analytics development for a P2P lending company.
Business Challenge
Our client, a P2P lending company, turned to us with a request to build a data warehouse to streamline multi-source data integration and quality management. It also required a robust solution to successfully handle rising data volumes and complexity.
Furthermore, our client was also aiming to enhance its value proposition with real-time analytics and dynamic risk profiling. The data analytics solution, however, had to ensure AI model fairness and transparency to mitigate compliance risks and avoid biased output.
SPD Technology’s Approach
We began our collaboration by developing a data governance framework, followed by a strategic data integration plan and data quality metrics. With those foundations laid down, we moved on to designing the data warehouse with extra consideration for security measures and access controls.
With the data warehouse design complete, we developed and deployed real-time data pipelines based on the defined data analytics metrics. We ensured the AI infrastructure remains scalable as the client’s operations grow while maintaining performance and accuracy. As for the AI models’ fairness and transparency, we applied fairness constraints, disparate impact analysis, and regularization methods, as well as local and global explainability techniques and bias audits.
Value Delivered
As a result, our client received a single solution that consolidated data from 15+ sources, while also reducing data inconsistencies by 40% and query speed by 60%. Our real-time data analytics model allowed for more granular risk profiling, with 10-second risk updates replacing hourly ones the company relied on before. In addition, our solution accelerated borrower-lender matching by 70%, thus improving customer experience.
Conclusion
Data analytics is one of the most impactful drivers of financial companies’ sustainable growth amidst a highly volatile market environment. Its use cases are ubiquitous, from enhancing risk management and improving fraud detection accuracy to maximizing capital and portfolio performance.
Leveraging data analytics in the financial services industry can help you ramp up a number of strategic advantages, from enhancing risk management and boosting customer retention to enabling data-driven strategic decision-making and optimizing operational costs. At the same time, implementing data analytics is a massive undertaking, especially if your IT infrastructure is complex. In such a case, your organization can benefit from data consulting services.
We, at SPD Technology, have the required technical expertise and experience to deliver financial data analytics services that turn data into a value-driving force for our clients. Get in touch with us to discuss how our expertise can secure sustainable growth opportunities for your business.
FAQ
- What is data analytics in finance?
Data analytics in finance involves collecting and analyzing data to streamline the organization’s operations, enable data-driven decision-making, provide personalized CX, and enhance risk management.
- How is data analytics used in finance?
You can use data analytics to:
- Provide personalized financial planning advice
- Identify the most efficient tax optimization strategies
- Enable predictive maintenance for ATMs
- Provide more granular credit risk assessments
- Automate first notice of loss
- Forecast cash flows and liquidity.