Financial Analytics

Understanding Financial Analytics

It might seem like a no-brainer that the financial industry and data analytics would go hand in hand. But many enterprises are still working toward “spinning straw into gold” — that is, turning the vast oceans of data available today into actionable insights relevant to business performance goals.

As Financial Executives cites, a recent report found only 14 percent of finance organizations are successful at turning these huge volumes of data coming in from their various transactional systems into valuable, usable insights. What are the consequences of failing to harness data in effective decision-making? Employee questions go unanswered. Potentially game-changing insights stay buried deep within rows of data. Forecasts lose accuracy. Decisions get delayed or must be made on the basis of scheduled reports rather than ad hoc analysis.

Use Cases for Financial Analytics

As you can imagine, the use cases for financial analytics today are as varied as the sources of data financial institutions are aiming to harness in decision-making.

Lenders are using analytics to detect patterns that alert them to potential sources of risk exposure — like customers most likely to become delinquent on loans. Specifically, machine learning algorithms as part of an artificial intelligence platform can learn to detect and predict certain warning signs of liability, then alert human decision-makers so they can decide how to best address these types of situations.

Financial services companies can also use analytics to identify and reduce customer churn, which continues to be a costly and frustrating phenomenon within the industry. As The Financial Brand cites, a report from Accenture found that banks see attrition rates north of 10 percent overall — and risk losing one out of every four new customers within the first year of the relationship. A major role of analytics is identifying promising segments, finding ways to boost loyalty, improving product offerings, and figuring out why people are churning before they continue to do so.

Examples of Financial Analytics Tools

As is true in most industries, there’s been a shift in the very ethos of analytics. Rather than treating analytics like the responsibility of specialized data teams, enterprises competing on data are democratizing it — that is, connecting a wide range of employees directly to the search tools they need to query data and generate interactive charts. So, instead of waiting for a monthly report from the data team, a mortgage broker could instead get their own answers in seconds — in a digestible, non-technical format — about product types and customer portfolios.

AI-driven tools with machine learning add another layer to financial analytics because they go beyond the questions employees are able to overtly ask. These algorithms can deep-dive into oceans of data to identify potentially useful patterns and anomalies, then bring them to the surface so human analysts can take a look and decide whether to act. Far from making human analysts obsolete, AI-driven platforms take over the time-consuming, manual work of mining for insights so analysts can spend their time working on more strategic tasks.

Challenges in Financial Analytics Today

As financial services enterprises work to increase access to data insights across their workforces, data silos may present a serious challenge. In systems with disparate data sources and legacy IT systems, silos may hinder user accessibility and even fuel different versions of the truth co-existing depending on which data source an employee is using. Creating a single version of the truth and eliminating data silos are just two of the advantages of operating on a platform that democratizes data.

According to Experian, here are some of the other predominant analytics challenges facing financial companies:

  • Dealing with the limitations of legacy systems and processes.
  • Establishing data governance and traceability of data back to its source.
  • Trying to bring together data from many different sources.
  • Integrating new tools with existing technology.
  • Ensuring compliance with strict regulatory requirements in the industry.

Enterprises able to successfully take advantage of financial analytics will gain a competitive advantage, while those unable to do so will struggle to capitalize on the value of all that data.

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