Data Science in Professional Services
These days, it seems everyone is talking about "data" in some form or fashion. Breaches and privacy command most of the headlines, but analytics is generating buzz in the professional financial services industry, as more firms explore ways to put data to work for their organisations and clients.
Truth is, data analytics already plays a role in traditional accounting. Historical financial information is routinely used to provide insights and examine the reasons for a company's past performance. While helpful, this reflective utilisation of data—known as descriptive and diagnostic analytics—fails to leverage the far more potent, forward-looking applications dubbed prescriptive and predictive analytics (whose current buzzwords are "AI" and "machine learning," which is actually a subset of AI).
With assistance from a data science team, accounting and finance advisors can use machine learning to power predictive analytics, which can help companies understand and plan for the future by identifying patterns in historical data. For instance, information from past accounts receivable transactions could be analysed to project accounts receivable balances and collection periods, enabling the organisation to better manage cash flow. This proactive use of data would allow businesses to anticipate risks and make decisions that yield better outcomes.
Prescriptive analytics, meanwhile, utilizes optimisation techniques and machine learning to assist companies in choosing the best option for a particular outcome. Using the same accounts receivable scenario, this foresighted analysis could be used to reduce collection periods and issue discounts for early payments.
To deliver this enhanced level of service, some professional financial services organisations have begun adding data scientists to their teams. These specialists extract, clean, organize, and analyse customer data, then share the insights and predictions with the firm's client-facing professionals, who have the best understanding of their customers' unique businesses. It's an iterative, back-and-forth process to understand clients' specific needs and to glean the most information from available data to design and deploy custom solutions with analytics products.
Of course, hiring a data scientist requires a substantial investment of time and resources, especially since there's a dearth of qualified candidates. The practice is also in its infancy, so early adopters are still determining the distinct role data science could and should play in the context of professional financial services. That said, the potential value that data specialists can deliver to clients of firms that employ them is profound. Some of the advantages include:
Facilitating better decisions. Data scientists can measure, track, and record performance metrics and other information across entire organisations, providing management with research-backed information to drive improved decision-making processes.
Directing actions based on trends. By analyzing an organisation's data and comparing it with that of competitors and others in the market, data scientists present recommendations that can help enhance business performance, cut costs, better engage customers, and ultimately increase profitability.
Mitigating risk and fraud. Trained to identify data that stands out in some way, data scientists can develop modeling that predicts the likelihood of fraudulent activity and create alerts that help ensure timely responses when data irregularities are recognized.
Delivering relevant products. Armed with the findings generated by data science, financial services professionals can help clients pinpoint when and where their products or services sell best, ensuring they provide customers what they want when they want it. This information can also be used to help companies develop new products and solutions to meet their customers' evolving needs.
As an example of how a professional financial services firm might utilize advanced data analytics to deliver more client value is if there’s considerable changes made to a tax legislation in your region. Proactive firms could run reports through specialized software and analyse which clients would be most affected by the tax changes. Then advisors could reach out to those customers directly and develop strategies to take advantage of potential savings throughout the year rather than waiting for the customer to request services at year-end. The result: a more satisfied client.
While speaking at a tech conference several years back, Peter Sondergaard, then executive vice president of research and advisory at Gartner Inc., quipped, "Information is the oil of the 21st century, and analytics is the combustion engine." If his words are to be believed, any business leader looking to move his or her company forward would do well to seek the services of a data-driven professional financial services provider to avoid being left behind.
This article was written by Paul Simpson, a data scientist at Elliott Davis, an independent firm associated with the Moore Global Network. © 2020. All rights reserved. Used with permission.