CNV3-I2-9- Application-‘Financial Analytics’ is the way forward

INTRODUCTION

In most of large companies in finance sector there are hundreds of millions of financial transactions on their ledgers. The heterogeneous data sets within company make timely financial analytics very complex and challenging. Their handling requires cutting edge financial algorithms, which should be based on data modeling standards, risk algorithms, consistency and scalability.

For business growth it is essential to generate timely financial, analyse it and disseminate accurate information with actionable insights at the decision points. The purpose of financial analytics is to collect the data accounting data, analyse it into powerful analytics with insights to take finance related decisions to mitigate risk, reduce costs and enhance profitability. It helps to bring more transparency and guides decision making process. The financial analytics essential for forecasting the future performance of the company covering forecasting of future cash flows, expenditures and revenue. It helps in modeling company’s capital structure and budgeting.

BACKDROP

During 2008 financial crisis, Lehman Brothers went bankrupt. At that point of time there was no process or structure available with any big banks or regulatory authorities to measure the risks in the subprime lending, securitization, and risk transfer. As a result, o body had an adequate picture of these risks and consequences of failure of financial institutions when the crisis hit. The shortcoming thereafter identified by the Basel Committee is the banks’ poor capability to aggregate risk exposures and its consequences across business lines and between legal entities.

After 2009, banking regulation and oversight has been strengthened with the Dodd-Frank act and the Basel III reform. With this reporting requirement have been considerably increased. However, the with the heterogeneity of the banks’ IT environment and the lack of a data algorithmic standard problem of aggregation of risk positions still exists.

Financial analytics has the important objective of keeping the track of a company’s financial plan, analysing the company’s performance, create forecasts based on market trends and calculate variances to take appropriate action. This requires a deep insight to explain the causes of variances. The key tasks under financial analytics covers;

• Analysis of financial results, forecasts, variances to visualise trends for future actions.
• Evolve the financial models.
• Help in budgeting process.
• Transactions reconciling using incoming and outgoing data.
• Use market researched data to support internal financial analysis.
• Keep updated on trends in market conditions and financial instruments.

Modern financial analytics come with a long list of promises such as deeper visibility into balance sheets, optimized forecasting, tighter expense management, better cash-conversion cycle, more profitable decisions and more informed calculations about risk.  Global enterprises like McAfee, Deloitte, and Wiley are seeing reductions in costs, gains in efficiency, and savings in time and labour, all with a surprisingly fast time-to-market.

Today, financial analytics are becoming essential problem-solving tools in the accounting and finance professions. The accounting and finance professionals are now expected to serve as business partners and experts who can use data analytics to inform recommendations on business strategy.

SCOPE

With financial analytics the end users can have an enhanced focus on financial functions of the organisation. The visibility on factors driving cost, revenue, cash flow and is better leading to enhanced shareholders’ value. It helps the businesses to plan for an action with insights on available financial data to resolve the business problems.

Technological advancements increase in demand for cloud-based services and need for analytics solutions among end users drive the global financial analytics market. The various reports published by leading global consultants confirms about the prospects of the financial analytics markets which includes market dynamics based on the regional analysis.

VIEWPOINT

The rise in requirement for higher transparency from stakeholders, expectations for effective partnering, change in regulatory environment, and continuous economic uncertainty, influence the financial analytics market. Furthermore, key analytical areas, such as profitability management, cost management, value for money analytics, business risk management, tax management, and regulatory compliance, encourage enterprises. Today leading enterprises are increasing their expenditure on analytical solutions to gain higher efficiency and valuable insights. Moreover, enterprises operating across the verticals, focus on employing talented and skilled employees to analyse huge volume of raw data.

It is predicted that the global financial analytics market will grow considerably, especially in Asia-Pacific region, owing to rise in awareness among end users. The end users have become well-informed in their respective domain to effectively tackle competition owing to constant changes in business and financial scenario. The role of financial management and analytics has transformed and expanded over the past few years. Currently, the demand for financial analytics is on continuous increase.

The rise in requirement for higher transparency from stakeholders, expectations for effective partnering, change in regulatory environment, and continuous economic uncertainty, influence the financial analytics market. Furthermore, key analytical areas, such as profitability management, cost management, value for money analytics, business risk management, tax management, and regulatory compliance, encourage enterprises. They should increase their expenditure on analytical solutions to gain higher efficiency and valuable insights. Moreover, enterprises operating across the verticals, focus on employing talented and skilled employees to analyse huge volume of raw data.

Organizations operating in the financial analytics industry are anticipated to deploy advanced financial analytical solutions across the world. At present, information is a key strategic asset used by organizations to compete in the market. Thus, end users are anticipated to capitalize this trend to acquire insights and build intelligence to respond more effectively to changes in the business environment.

DATA ECOSYTEM

For financial analytics, the business must have a matching data ecosystem, which in simple word is data collection devices, analytical tools, applications software and skilled people with deep insights in analysed data. Data ecosystems provide companies with data that they rely on to understand their customers (internal and external) and to make better pricing, operations, marketing and other business decisions. However, data ecosystems capture data to give useful insights.

Ecosystems, in the simple words are an information technology environment. Currently they are relatively centralized and static. However, the computing and web have changed it to dynamic in time and usage. Python is now becoming the number one programming language for data science. Python’s being simple and having high readability, it is gaining its popularity in the financial industry. Some are using R-programming language, which is a free, open-source software used for heavy statistical computing. Many statisticians use R because it produces plots and graphics that are ready for publication, down to the correct mathematical notation and formulae. As more and more data are captured for usage in the organizations infrastructure for use to collect data should be scalable to constantly adapt and change. There is no single ‘data ecosystem’ solution for all. It differs with every business, referred to as a technology stack which includes various hardware and software data collection, storage and analyse to take appropriate action on analysed data. The right type of ‘Data Ecosystem’ helps the teams to calculate performance metrics, coordinate with multiple data sources, track the user cohorts and provide tools to automate the process of conducting analysis

The financial analytics platforms are of great help in enhancing financial data user engagement, identifying hidden relationships in the data, analysing individual users, real time alerts to teams for changes and finally integrating with other applications in the system.

CONCLUSION

In nutshell ‘Financial Analytics’ helps to analyse the organisational performance, manage investment decisions, measure the value of assets (both tangible and intangible), evaluate the investments decisions leading to increase in profits.

CA Aarti Patki, Asst Professor, NMIMS, Navi Mumbai