Role of data analytics in financial services industry

If data is the soul of the financial industry, data analysis is the body that is required for the industry’s growth.

Data analysis ensures that information is communicated clearly to stakeholders of a company, allowing them to make informed decisions based on the insights gathered.

Over the years, financial institutions and banks have been processing large amounts of data with the help of machine learning and AI to meet the needs of their customers. They are in fact forerunners in Big Data.

In this blog, we will talk more about the role of data analytics in the financial services industry and how it has acted as a critical resource for finance companies.

Data analytics helps in creating better financial models

Financial institutions generate a lot of data. Data is generated when customers make transactions, when they interact with the bank and when they use the bank’s website. The problem is that this data is hard to process due to its volume. So, a lot of time is wasted by finance teams in processing and extracting insights from it.

Fortunately, data analytics helps finance managers and executives in converting huge piles of structured and unstructured data into valuable insights.

The insights provide a detailed view of key performance indicators such as the revenue generated, payroll cost, net income, and other expenditures. The information gathered by the KPIs enables finance managers to focus on the big picture and help them make changes in the financial model.

Overall, accurate data insights help finance teams generate a stable finance model with minimum risks.

Data analytics helps managers make data-driven decisions

The finance industry has transformed the way they operate in recent times. Most companies have started incorporating solutions such as Jedox to make data-driven decisions.

These solutions are not only error-free and accurate, but they have also significantly reduced the cost of financial operations. For continuous forecasting and smart insights, these solutions can make use of advanced machine learning and artificial intelligence (AI).

Machine learning is a subfield of computer science and artificial intelligence that deals with the design and development of algorithms that allow computers to learn from experience without being explicitly programmed.

In the finance industry, machine learning algorithms analyze historical data from sessions where users share information on the events they have handled. Analyzing this data not only helps the organization’s targeting and offers, but also can also be used to create better campaigns.

Data analytics can help managers discover new opportunities

Analysts are BI power users who use centralized company data in conjunction with powerful data analysis tools to determine where opportunities for improvement exist and what strategic recommendations to make to company leadership.

By combining financial data with operations, marketing, and sales data, users can gain insights from which to make decisions and understand factors that influence profit and loss.

With the power of predictive analysis and prescriptive companies, users can make projections of future events and reveals why they should take a particular action.

How does predictive analytics work?

Predictive analysis, as the name suggests, is concerned with predicting and comprehending future events with the help of data mining, statistical modelling and machine learning algorithms.

It assists finance managers in analyzing data patterns and trends, which allows them to set realistic goals, plan effectively, manage performance and expectations, and avoid risks.

Data analytics helps in risk management

In financial institutions, data analytics is used to detect fraud. Big data analytics can help businesses deploy and integrate new and rich data types in order to generate new and more sophisticated analyses against fraudsters and to continuously improve the loop of legacy approaches to the war on fraud.

These analytics have proven to be extremely effective in exposing not only the fraudsters themselves, but also their networks and the people, places, and processes with which they have or will have contact in a few test cases.

Data analytics can also be used to determine how a portfolio of assets, such as stocks, bonds, futures, options, and so on, or a single asset, responds to various scenarios. This can help to reduce the overall risk of the portfolio and improve its performance.

Lastly, Analytics is used by banks and financial institutions to manage the risk associated with the loans they make. This is accomplished by closely monitoring the data they collect on individual customers. This information may include, but is not limited to:

The credit score of the customer
Use of credit cards (how much you owe)
Amounts owed on various credit cards (total debt)
Sums owed on various types of credit (total debt/total credit)

Credit risk analysis is the examination of historical data in order to determine a borrower’s creditworthiness or to assess the risk involved in loan granting. The findings of the analysis assist banks and financial institutions in assessing their own and their customers’ risks.

Conclusion

Now that you know data analytics is an invaluable tool for organizations in the financial sector, you need a solution that helps you succeed in today’s changing market landscape.

In Jedox, you can use fast self-service data analysis and flexible management reporting. Alternatively, you can easily integrate Jedox with your existing BI solution to increase value through integrated planning and analytics.

Develop a data-driven culture in your organization. Empower all business functions with self-service analytics and reporting that is simple to use and built on a common data foundation. Everyone has the tools necessary to conduct their own analysis on the web or directly in Microsoft Excel using Jedox Views. It’s also quick and simple to create professional-looking reports using standard formatting and your company’s branding.