The COVID-19 pandemic has hit most institutions hard around the globe, but one industry in particular that has been impacted is the financial sector. With many people out of work, they are turning to banks for loans and this has resulted in an overload, or flood if you will, of data.
What caused the flood of data in finance?
One explanation for this influx is the CARES Act enacted by the Trump administration. This includes the Paycheck Protection Program (PPP). The $349 billion initiative allows businesses devastated by the coronavirus to apply for loans to hire back employees and pay employees during this tumultuous time. As applications opened for assistance, businesses rushed to financial institutions to apply for loans, flooding lenders with calls, emails, resulting in an overload of data. In fact, after the first hour, Bank of America received 10,000 loan applications! By the following Monday, they had 177,000 applications which totaled $32.6 billion in loans or 10 percent of the allotted funds.
The CARES Act is a necessity because, according to Financial Stability Oversight Council chairman Jelena McWilliams, “the brunt of this economic impact is going to fall hardest and fastest on consumers, small businesses, independent contractors, low-income borrowers, and hourly workers.”
COVID-19 shifting finance
Like many services in today’s COVID-19 reality that have gone digital, remote banking is now also preferred by many people. This fact coupled with the fact that mobile phones are a lifeline during this crisis, financial institutions have no choice but to provide additional mobile solutions and digital on-boarding for new customers. The shift to digital has many benefits, such as easier transactions and connectivity, but there are also some drawbacks as the multitude of data can actually overwhelm organizations, namely the BI teams who must process the data.
BI teams in financial institutions are overwhelmed
With so much data processed in multiple systems throughout the BI environment, it is difficult for BI teams to do their jobs as they must spend most of their time reconciling data and are unable to work productively and deliver quickly to the business.
BI teams in financial institutions are challenged on a daily basis with being able to quickly assess compliance risks, support risk reduction and prepare for economic challenges. They need to use power predictive analytics for optimized trade routing. BI teams must be able to gain a better understanding of the transformations the data is going through in their environment in order to gain visibility into their data flows and thus make better data-driven decisions.
Automated data lineage on the cloud: helping finance with risk management, regulations, financial crime
Timeliness is a challenge in financial institutions that use many BI systems. They need to track and measure governance, deal with cyber threats and fraud in real time, and easily meet regulatory requirements.
Financial institutions such as banks, investment firms, and credit institutions are discovering the massive value of automation for BI as they are able to find and understand their data faster and more accurately. Farm Credit Services of America (FCSA), a financial cooperative, boasts about just how much time their BI team saves regularly with automated data lineage and discovery – they literally cut down the amount of time it takes them to conduct impact analysis from months to a day.
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The importance of automation for BI teams in times of crisis
With the current reality of COVID-19, the world has had to adjust and transform accordingly. The way in which business is conducted has moved to an almost completely digital form and this has greatly increased the amount of data produced. In order to correctly handle and understand this flood of data, reliance on automated BI tools is vital. With automation, financial institutions are better equipped to face their challenges head-on and deal with risk reduction and mitigation, regulatory assessments and compliances, mitigation of rogue trading, market abuse, as well as fraud.
