Demystifying Finance Analytics – Unlocking the Value of Data Driven Decision Making
Finance is one of the most critical function in any organisation. Finance management requires lot of effort and time, but with latest in Data Science, now one can rapidlyanalysefinancial statements.
In contemporary highly competitive business environment, companies require much more from the Financial Data other than accurate financial statements and financial reports. They need to have progressive forecasts as well that can help take them strategic decisions for tomorrow by improving upon day-to-day decision-making in real time. In short, they need finance analytics.
Why finance analytics?
With the passage of time, companies have realised that a right approach to understanding of Finance can open many aspects of business, beyond its traditional role of providing a standard set of financial reports as required by law.
Now, every business leader expects from his CFOs for a better partnership in strategic decision making and actionable insight. This is indeed a big opportunity however it’s a big challenge.
Finance analytics better equips CFOs with the tools and techniques which can help him make sense of an increasingly complex world. By integrating internal financial information with operational data as well as external information such as social media, demographics and big data, finance analytics helps in addressingimportant business questions in an easy manner at a faster pace and with great accuracy.
To summarise, finance analytics can help to answerfollowing :
- Risk exposure with specific customers, how does each customer relationship affect working capital?
- Streamline business processes to make them more efficient
- Investing in the right opportunities based on capital and/or revenue, and its impact our key value drivers
- Profitability of products and services across sales channels and customers
- Latest margins on each customer segment – now and in future and how are those margins affected by today’s choice making.
- Effect of future events on stock price
How it works
Financial Data from different sources like financial statements is inserted into the digital platform through many connectors available in the Data Centre module of the software . The Data Centre provides data entry into the platform and helps in connecting with multiple databases to create data service and elastic search. After Data Input, financial data is cleaned and prepared using Data Preparation Module of the platform for e.g. replacement of missing or inaccurate data and other transformations that are either provided or inbuilt in the Software.
This data is then used as a data service or data store in the subsequent module for prediction and visualization. Various Algorithms are built in the platform that can help management in forecasting revenue, sales and growth of the business. Actual and Forecasted data can then be fetched and used for visualization in the form of Dashboards and reports. The Dashboard has multiple charting components for visualization of financial data. This platform / software can be customised to give access to self-service reports to business user so that they can analyse data using simple drag & drop functionality without much of external IT assistance. It also enables the user to explore data upto any granularity and drill down to get a deeper understanding of their data. There are companies which provide conversational analytics by which you can access data from your system or mobile by asking queries via text or voice command and can add the results to Dashboards.
What is required on the part of management
To begin with, what management requires is to start from the endpoint that is Start by identifying critical business problems that need to be solved, andthen work backwards to see how finance analytics can help. This may revealproblems that are not even known as well as potential new sourcesof valuable information that have not been tapped uptil now.Focus on critical business areas andstrategic challenges that are likely to benefit from Finance’s insight. As experience is acquired with finance analytics, constantly look forways to use it more effectively and strategically.
Building A Career In Financial Analytics
Increase in number of Banking Frauds, rise in cyber crimes in financial world, risk management are some of the reasons why finance analytics is booming as a career among the coming generation.
Building models for credit scoring to identify risky customers, identifying fraudulent transactions using pattern detection, identifying cross-sell and up-sell opportunities – these are all examples of application of analytics in the financial services sector.
To make it as a career, strong quantitative skills are required in this field. A Financial analyst deals with large volumes of data, numerous spread-sheets and variety of financial and statistical measures.A knowledge of analytical tools such as SAS, Excel, SPSS or R is therefore essential.Domain knowledge is essential for a successful application of analytics to any financial problem. A knowledge of the banking, credit-card and insurance industries is therefore very desirable in financial analytics.So if you are planning to choose a career in finance analytics, you must seek to enhance your skills by
- Developing and evolving an understanding of the financial services domain
- Learning the various analytic techniques being widely used in financial services
For more insights about the basics of Finance Analytics, you may refer to following sites –
We will meet soon with further insight on Finance Analytics.
Keep reading & enjoy this and further posts of mine on Finance Analytics.
Have a great time!
MS. RACHNA KATHURIA
Post: Assistant Professor
Jims Kalkaji
