Analytics For Finance

Analytics For Finance: Optimize Strategic Performance And Resource Utilization

Critical Issues Facing Analytics For Finance

Chief Financial Officers (CFOs) today find themselves under tremendous pressure to execute Financial Planning and Analysis (FP&A) processes faster in order to discover insights that would augment the company’s future results.

Many finance departments find themselves tasked with faster closing times, real-time consolidation and elimination of manual tasks, in the interest of overall business agility. So the role of the FP&A professional is not only under more focus but also changing. He is expected to help decision-makers within his company optimize strategic performance and resource utilization.

It is often found that business managers and the finance teams are often not on the same page. There needs to be collaboration between the two in order to apply finance analytics. Needless to say, those making the decisions need data analysis to tackle problems and deveop finance strategies.

Some of the challenges being faced by this industry are:

  • Low confidence on data
  • Quick fixes without overall business goal in mind
  • Analytical systems in silos
  • Poor analytical models
  • Lack of analytical skills

Finance Analytics: Present Situation

From a mere tool or a function in an organization, Big Data and data analytics have given the world of Business, Finance Services and Insurance (BFSI) the larger role of a strategic support function.

The world of financial planning and analysis is changing. It’s not only expanding the role of a Finance professional by making him help line managers and key decision-makers optimize strategic performance, resource utilization, and profitability, but also given him powerful tools to help him make it possible to enhance his contributions to the organization – both in terms of the breadth of contribution and the level of output achievable in a given timeframe.

Financial Technology combined with data analytics led by advances in tech today like machine learning has disrupted the banking industry.

While a handful of organizations are incorporating such modern techniques in their budgeting process, the number is still small. The use of Predictive Analytics or demand planning are at very early stages of adoption.

Questions that need answers are: what role does analytics for finance play in  business? How does it help in taking quality decisions? What value does finance analytics really add to your organization?

Reasons For Critical Issues In Data Analytics For Finance

The traditional instrument of a BFSI officer so far has been the spreadsheet. Many companies continue with this static process used for decades for budgeting, forecasting and reporting.

Most fix the time frame of a year for such purposes. But the spreadsheet can get much more sophisticated with data analytics. The process of evolvement of the FP&A professional and his toolkit is still slow.

We Provide Finance Analytics

A bouquet of modern Business Intelligence (BI) and Analytics techniques. As part of our proactive partnership with FIs, our data scientists utilize the behavioral, demographic, and historical purchase data points in a customer’s journey to predict the probability of a customer’s response to a promotion, for example.

The focus is on the customer, and augmenting his experience. We help banking and non-banking players transform their data into an opportunity to generate new revenue opportunities.

For example, our data analytics solutions can be used by FIs to make their marketing campaigns more personalized.

Many think that predictive analytics is a must for the digital transformation of finance. We at Express Analytics offer predictive analytics solution where we examine historical and new data to assess what’s relevant to your company, irrespective of the area of finance it is in to to help with your decision-making.

BFSIs can use finance analytics for the following:

  • Customer Lifetime Value (CLV) prediction: To help understand all the value it can derive from its total relationship with a customer.
  • Customer segmentation: The segmentation of customers for the successful allocation of marketing resources, and to help in the retention of customers.
  • Customer support: Superior customer support service is the foundation for a healthy, long-term relationship with clients. This is where data analytics can play a vital role in tracking complaints, suggestions to keep the customer happy.

Benefits Of Finance Analytics

Financial analysis when a part of financial planning uses the output from planning to assess the profitability and the stability of the enterprise. In this process, companies also use various benchmarks to help them take business decisions.

Modern BI and Analytics will replace the spreadsheet. Advanced techniques such as ‘rolling forecast’ can be implemented, making it possible for much faster predictions over shorter periods. Analytics makes any BFSI enterprise “smart”, and customers “smarter”, because predictive analytics throws up estimated services that a customer may want.

The areas where our analytics tools and solutions can be deployed are:

  • Cost management
  • Forecasting
  • Profitability management
  • Regulatory compliance
  • PCustomer management

Some of the questions our finance analytics can help answer, include:

  • Are we investing in the right opportunities?
  • What is our risk exposure with specific customers?
  • How profitable are my products and services?
  • Which customer segments offer the largest margins?
  • PCustomer management

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