Essentially, business intelligence is a set of tools for the transformation of raw data into actionable insights that provide a comprehensive view of operations or a specific pipeline. It comprises three levels: tracking, which monitors current metrics; inferring, which interprets and identifies patterns within the data; and projecting, which uses these patterns to predict future trends and outcomes. In financial software development, business intelligence involves integrating data analytics tools and techniques into existing banking systems. Business intelligence in banking can explain customer behaviors, streamline processes, detect fraud, and assist with regulatory compliance, ultimately promoting growth.
Let's delve into each of these three levels of business intelligence applied to six key metrics in banking, how they function, and what benefits they bring.
Track
Recording the value of a metric over time might seem like a trivial task. However, it is far from it due to the complexity and volume of data involved in finance, the need for real-time accuracy, and the integration across diverse banking systems. Specialized banking software development services become indispensable, offering tailor-made solutions that enhance the efficacy of financial data management. Effective tracking requires robust systems capable of handling large datasets, ensuring data integrity, and providing accessible insights promptly.
Implementing an effective tracking solution is the most beneficial when applied to these metrics:
Service usage patterns
In the banking sector, customer data is often dispersed across various systems and platforms, such as transaction processing systems, CRM software, online banking apps, and feedback management tools. Integrating multiple customer data systems into one view allows for tracking interactions, transactions, and feedback, providing a comprehensive understanding of customer behavior. Business intelligence in banking industry is then used to clean, organize, and analyze this data. This enables banks to tailor products, enhance satisfaction, implement targeted marketing, identify at-risk customers, and take proactive measures to improve retention.
Building a business intelligence system to analyze this data paves the way for implementing personalized banking that would incorporate it into further services.
Read more: A guide to personalized banking implementation
Real-time compliance status updates
Business intelligence systems that automate reporting collect, process, and analyze vast amounts of regulatory and performance data. These systems ensure accuracy, reduce manual errors, and provide real-time updates, facilitating seamless compliance with regulatory requirements. A streamlined reporting system can significantly improve compliance. According to Gartner, 52% of banking executives report encountering compliance issues due to data problems.
The real-time capability of these systems is particularly crucial. As data is collected and analyzed, BI systems can immediately update the institution's compliance status. This instant feedback loop allows for swift identification of compliance issues as they arise, enabling banks to address potential problems before they result in non-compliance. This real-time updating is vital not only for maintaining regulatory compliance but also for providing stakeholders with up-to-date, transparent information about the institution's compliance health.
Risk estimation
Tracking various risk types—credit, market, operational, and compliance—is a crucial element of business intelligence in the banking sector. It integrates multiple facets of the organization. By analyzing trends and patterns in historical and current data, predictive analytics can forecast potential threats, such as credit defaults, market volatility, or operational failures. This tracking enables banks to identify risks early, implement mitigation strategies, and safeguard assets, enhancing overall financial stability.
Infer
Once all relevant data is properly collected, business intelligence software uses data analysis tools to infer more information than the initial data contains. The tools of the second level transform disparate data points into comprehensive insights by identifying patterns, correlations, and trends that are not immediately apparent. For instance, raw transaction data can be analyzed to infer overall financial performance, customer behavior trends, and risk exposure levels.
Financial performance
Business intelligence tools aggregate cash flow data from sales, expenses, investments, and loans and identify trends and patterns over time. They are then able to estimate the organization’s liquidity, profitability, and financial stability. This analysis helps banks understand their ability to generate revenue, meet obligations, and invest in growth, providing a comprehensive view of financial health and performance.
Market position
Latest-generation business intelligence tools can pull data from market reports, financial statements, customer reviews, and even social media to infer a complete picture of the organization’s market position. They analyze a bank's standing compared to its competitors in aspects like market share, customer satisfaction, product offerings, and financial performance. This analysis provides a complete perspective on the bank's relative strengths and weaknesses, emerging market opportunities, and potential threats. It enables the bank to strategize effectively and enhance its competitive position.
Asset portfolio health
Market prices, financial statements, and economic indicators can be used to assess the health of a financial portfolio. Economic indicators, such as interest rates, inflation rates, and GDP growth, help contextualize the performance of assets within broader economic trends. By analyzing this data, business intelligence tools can identify underperforming assets that may be dragging down the portfolio’s overall performance, as well as highlight emerging risks such as market volatility or economic downturns.
Additionally, these tools can uncover growth opportunities by pinpointing assets or sectors poised for appreciation based on economic forecasts and market trends. This aspect of business intelligence in banking analysis equips managers with the necessary information to make informed decisions on asset allocation, ensuring a balanced and diversified portfolio. It also aids in developing robust risk management strategies to mitigate potential losses and in crafting effective investment strategies to maximize returns, thereby optimizing the overall health and performance of the financial portfolio.
Read more: Business Intelligence in finance: Applications for revenue growth
Project
Business intelligence systems can go one step further and use historical data to predict future outcomes or allow banks to simulate hypothetical scenarios and understand potential impacts. This information aids in allocating resources more effectively, directing efforts to areas with high anticipated returns or needs.
Future liquidity needs
Banks can use algorithms to analyze cash inflow and outflow patterns across various operations, integrating historical data with real-time financial information and external factors like macroeconomic forecasts. For instance, a bank might use time-series analysis to identify seasonal cash flow patterns, such as increased inflow during certain months due to tax returns or holiday seasons. Predictive models may also incorporate upcoming financial commitments, like planned investments or anticipated large withdrawals, to provide a comprehensive view of future cash positions.
Cost and needs of future regulatory compliance
Advanced business intelligence tools can transform the approach to regulatory compliance from reactive to predictive. They assist in setting more realistic expectations and budgets for regulatory compliance and identify problem areas that impede regulation adaptation. This approach considers various factors, such as the financial costs of meeting regulatory requirements, resources allocated for compliance initiatives, and the effectiveness of these actions.
Further enhancing this predictive framework is the integration of NLP models. These models analyze text data from various sources, notably regulatory bodies' press releases and public communications. This proactive approach enables banks to begin implementing changes early, particularly if the required changes are substantial.
How N-iX approaches building banking BI solutions
At N-iX, we build impactful BI solutions by implementing a strategic and structured process that encompasses the entire data management lifecycle—from initial discovery to deployment. Over 21 years of experience, we have successfully delivered over 30 data projects. Here’s how our experts do it:
- Discovery: Our experts begin by understanding the client's existing data flows and systems. The goal is to design an effective data strategy that aligns with the client's business objectives and addresses their challenges.
- Designing a data strategy: We develop a strategy that outlines the steps to develop a modern data platform that can support advanced data analytics capabilities, focusing on delivering precise and intuitive reports.
- Implementing: N-iX leverages cloud technologies for data management and analytics.
- Improving data security and governance: A significant aspect of our approach is establishing robust data governance. This ensures the security and integrity of data across all stages of processing and analytics.
The final outcome of N-iX’s approach is a BI solution that not only meets the specific needs of the client but also enhances the client's internal capabilities to analyze performance and make data-driven decisions.
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