These days, it seems that everyone is talking about “data” in one form or another. Privacy breaches make the headlines, but in the professional financial services sector, it’s analytics that’s the talk of the town. Indeed, more and more companies are exploring how to use data to serve their organizations and their customers.
In fact, data analysis already plays a role in traditional accounting. Historical financial information is commonly used to gain insight into and explain a company’s past performance. While useful, this thoughtful use of data, also known as descriptive and diagnostic analysis, fails to exploit the much more powerful and forward-looking applications known as prescriptive and predictive analysis. What we’re talking about here are buzzwords like “artificial intelligence” (AI) and “machine learning”, which is actually a subset of AI.
With the help of a team of data scientists, accounting and financial advisors can use machine learning to power predictive analytics. The latter can help companies understand and plan for the future by finding patterns in historical data. For example, accounts receivable information on past transactions could be analyzed to predict receivable balances and collection periods. In this way, an organization can better manage its cash flow. This proactive use of data enables companies to anticipate risks and make decisions that ultimately deliver better results.
Prescriptive analysis, on the other hand, uses optimization techniques and machine learning to help organizations choose the best option to achieve a particular result. Using the same accounts receivable scenario, this forward-looking analysis could be used to reduce collection times and grant early payment discounts.
To provide this enhanced level of service, some professional financial services organizations have begun hiring data scientists. These specialists extract, cleanse, organize and analyze customer data, then share information and forecasts with business professionals. These are the people who communicate with customers. This puts them in the best position to understand their customers’ unique businesses. This iterative back-and-forth process aims to understand customers’ precise needs and extract the maximum information from the available data. Thanks to analytics products, organizations can design and deploy customized solutions.
Of course, hiring a data scientist requires a substantial investment of time and resources, especially as there is a shortage of qualified candidates. What’s more, the practice is still in its infancy, so early adopters are still determining the distinct role data science could and should play in professional financial services. That said, data specialists can bring significant potential value to the customers of the companies that employ them.
Here are just a few of the benefits:
Help you make better decisions. Data scientists can measure, track and record performance and other information across organizations. In this way, they provide management with research-based information to improve decision-making processes.
Direct the measures to be taken according to trends. By analyzing an organization’s data and comparing it with that of its competitors and other market players, data scientists come up with recommendations. These can help improve business performance, reduce costs, better engage customers and, ultimately, increase profitability.
Mitigate risk and fraud. Data scientists are trained to find data that stands out in some way. They can develop models that predict the likelihood of fraudulent activity, and create alerts that ensure rapid responses when data irregularities are recognized.
Supply suitable products. Thanks to the results generated by data science, financial services professionals can help customers determine when and where their products or services sell best. This ensures that customers get what they want, when they want it. This information can also be used to help companies develop new products and solutions that meet the evolving needs of their customers.
For example, a professional financial services company can use advanced data analytics to deliver more value to customers when significant changes are made to tax legislation in a region. Proactive companies could run reports using specialized software and analyze which customers would be most affected by such tax changes. Rather than waiting for the customer to request services at the end of the year, advisors could approach them directly after developing strategies for saving throughout the year. In the end, customers are more satisfied.
At a technical conference a few years ago, Peter Sondergaard, then Vice-President and Director of Research and Consulting at Gartner Inc. said: “Information is the oil of the 21st century, and analytics is the engine of combustion. “If his words are anything to go by, any entrepreneur who wants to take his business to the next level and doesn’t want to be left behind should enlist the services of a data-driven professional financial services provider.
This article on data science, was written by Paul Simpson, a data scientist at Elliott Davis, an independent company associated with the Moore Global network . © 2020. All rights reserved. Used with permission.
