Accounting and finance exist in a world of numbers. It makes sense that data analytics could play an important role in shaping the financial industry. But how?
With data technology growing at a rapid pace, the impact on financial institutions has never been more pronounced. In this article, we examine the impact of data analytic software in the financial industry and examine how it impacts both the business and customer experience.
Data in Finance
First, it’s important to understand that data analytics has always been an important part of accounting and finance. The financial industry uses enormous quantities of information to make future forecasts, and just generally advise their customers.
Historically, this work has been completed entirely, or at least mostly by hand. The rise of analytic software changes the equation significantly. What once took entire afternoons can now be accomplished with a series of clicks.
In fact, data analytic software, coupled with other technologies like AI, has made such a significant splash in the world of accounting that decades ago, some experts were predicting that accounting as a career path would soon cease to exist entirely.
Of course, accountants have not been replaced by machines. They just move faster, more efficiently, able to focus their time on high-value tasks that might otherwise have been out of reach in a pre-data world.
Better Customer Experience
Business technology, no matter the industry, is usually incorporated either directly or indirectly with the intention of better serving customers. Even backend technology is implemented to allow businesses to run more effectively, thus allowing them to apply more time to customer satisfaction-driven activities.
More narrowly speaking, data-driven fintech allows people working in the financial industry to take a granular look at their customer’s financial habits, identify patterns, and make recommendations accordingly.
For example, a financial advisor could use data-driven algorithms to analyze a customer’s spending habits, compare these with their financial goals, and form a highly detailed budget recommendation designed to keep that customer on track.
Data can also improve a financial institution’s internal operations. Scenario: A financial firm is trying to reduce customer churn. Their strategy is to identify accounts that seem poised to leave, and try to work with them to see how their experience can be improved.
This is a large firm. Manually sifting through hundreds of accounts looking for churn risk is impractical and unlikely to produce results. Solution?
Algorithms. Using data-driven algorithms, this firm can input churn-related behaviors — low account activity, modest engagement levels, etc — and use the data to automatically flag high-risk accounts.
This is just one of the many ways data can make it easier for any business to operate. Similar tactics can be used to hone a financial institution’s marketing strategy or monitor the efficacy of its customer onboarding efforts.
Essentially, any business activity can be evaluated quickly and effectively by the right data-driven processes.
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