The growing competition amongst various businesses for market share in their sector has made execution one of the key factors for success. The availability and usage of data are one of the key ingredients for decision-making. This is evident from the fact that the global big data industry was worth a whopping $ 138. 9 billion in 2020. Unsurprisingly, financial analytics is emerging as one of the most prominent forms of data analytics. Data in its raw form might seem incoherent and useless to a firm but structured finance analytics may hold the key to value unlocking and stay ahead of the competition.
What is financial analytics?
Financial Analytics involves the usage of data and converting the data into intelligible business analytics and reporting that guides specific questions for an organization. Financial Analytics is useful in the conversion of incoherent data into purposeful data using various tools that may be available with an organization. Various organizations including commercial banks, investment banking companies, etc. will find this analysis useful. Structured financial analytics can be considered a key support system and driver for any business activity.
Why is financial analytics gaining importance?
- Overcoming time constraints:
Decision-making needs to be increasingly quick amongst the growing competition. Globalization has only led to higher competition for many firms, including commercial banks and investment banks. Structured Financial Analysis allows institutes to view the data clearly and concisely and allow them to make quick decisions based on the data. Financial analytics can transform complex data into a simple to understand and simple manner allowing for easy comparison between multiple firms, faster decision making and enabling savings in terms of both cost and time for the company.
- Efficient allocation of resources:
Commercial Banks, Investment Banking companies and even Private Equity companies have limited resources even though they have various avenues to invest and lend. Financial Analytics may use various tools including ratio analysis, comparative financial statements, etc. to enable the companies to make efficient allocation of their resources. This may include investing in a start-up or lending to a particular sector of the economy. Many sectors are often cyclical and go through their own economic upcycles and down cycles. Structured Financial analytics can help identify those trends and enable commercial banks to improve their lending policies, reducing chances of defaults, and allowing them to set proper interest rates as well as improving their profitability.
- Cash Flow analysis and projections:
Cash-flow, as well as cash-flow projection, are often the base on which investment banking companies or financial companies either invest or provide financial leverage. Financial analytics through the use of automated systems and Artificial Intelligence can allow the drawing up of various cash flow projections and analyses to aid the institution in decision making. Stress testing can be carried out based on the worst-case financial projections that would allow the investing or lending company to analyse the maximum loss they could suffer. Analytics ultimately aid in a better decision-making process for the company and improved profitability.
- Increased efficiency:
Structured Finance Analytics in combination with Artificial Intelligence or automated software would increase the efficiency of any company. Financial Institutes can consider automated financial spreading as a mode of financial analytics wherein easy comparison of the financial statements of various companies can be done in a similar format. It can set predefined criteria for different industries and easily filter companies that meet those criteria with the use of financial data analytics. Transactions like rating analysis, due diligence, risk management, etc. can see highly increased efficiency due to the easy availability of actionable data.
Conclusion:
Structured Finance Analytics can aid and assist any company to make better decisions in a shorter time frame. While data has become highly relevant to decision making, the sheer number of data available often leads to delayed decision making. Financial Analytics would enable the conversion of data into actionable data allowing a quicker turnaround time. Companies like Acuity Knowledge Partners assist Commercial and Investment banks through financial analytics and tools like BEAT that leverage technology to extract and sort relevant data as well as create an appropriate report to allow for easy usage of data. Financial Analytics is useful from debt-financed raising to a start-up pitch and its widespread usage and relevance makes it highly important.
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