Data gives the power to see unearth hidden insights. There is an opportunity at every point of business, and the financial industry is a great example. Every financial transaction, every customer interaction, and every part of the supply chain contributes to data insights. Moreover, financial data analytics provides the potential to process data instantly, convert them into insights, build products faster and detect frauds.

Data analytics enables businesses to forecast their future and react proactively. Banks and insurance companies use financial data analytics solutions to reduce customer churn and decrease financial failures. Also, data analytics solutions pave the way for new ideas, new products, and new revenue streams. AI-based predictive analytics is helpful to increase the average profit and customer lifetime value by two times.

Below are some of the uses of financial data analytics:

• Identify future markets
• Find new investment hotspots
• Minimize compliance risks
• Prioritize claims and manage them better

Here is an article on how financial data analytics is useful.

1) Efficient underwriting with financial data analytics

Banking underwriters and insurance underwriters provide detailed credit analysis and evaluate the risk and exposures. Financial data analytics methods are applied to analyze balance sheets and analyze parameters such as tangible net worth, the ratio of debt to net worth, and available liquidity. Analysis of income statements includes revenue trends, debt service coverage, gross margin, and profitability. Besides, natural language understanding solutions enable better social media profiling of customers and better risk assessment with a greater number of data sources.

2) Leverage data

Banking executives need supporting data to make any important decision. So, analytics solutions help C-suite executives in data-informed decision-making. Financial data analytics methods use historical data to predict outcomes and suggest necessary action steps.

Banks and financial institutions frame data-driven financial offers based on customer profiles. For this reason, the customers can be organized as high-value customers and low-value customers.

3) Cash Demand Forecasting & ATM Placement

Data analytics serve as a significant tool in forecasting the minimum cash requirements at each ATM of a bank. The differences in cash withdrawals during weekends (including holidays) and weekdays is a major factor in deciding the cash loading at ATMs.

Also, analytics solutions help in determining the placement of ATMs across different places of the city. The solutions consider data such as population density, road traffic, important road junctions nearby, and pedestrian density while ranking the ATM placement.

4) Find claim discrepancies with financial data analytics

Data analytics solutions help insurance companies identify potential anomalies in claims. For example, the company can find life insurance policies with more than one claim, claims from lapsed accounts, and policies with claims exceeding the provisions.

5) Basel Compliance and Analysis

Basel Accords are the international banking regulations issued by the Basel Committee on Banking Supervision. Complying with the different Basel frameworks is an important step for banks and financial institutions. Basel compliance solutions involve analysis of the probability of default (PD), loss given default (LGD), and exposure at default (EAD). The solutions help to determine Expected Credit Loss and enable the banks to understand portfolios better. Consequently, the solutions help banks to decide the credit risk probability of different portfolios.

6) Customer and transaction analysis

Data analytics solutions help banks to classify customers and provide customer-driven financial plans. Also, the analytics solutions help the financial institutions to predict customer payment behavior, reduce payment delays, and generate better revenue. The payment gateway tags the payment method as “Experiencing high failures. Please try another payment method” when a payment method is experiencing failures.

7) Augmented Financial Data Analytics

Augmented analytics solutions employ machine learning and natural language processing to automate data analysis. In addition, it eliminates human errors in financial processes using automated analyses and autonomous ETL.

8) Stock Market Analysis

Data analytics solutions are useful in the stock market and investment analysis. Also, they enable improved investment decision-making. The solution involves the analysis of historical data from several business exchanges. As a result, the stock exchange provides better customer campaigns, customer targeting, and customer offers.