The fall of Silicon Valley Bank has bank clients looking to financial institutions for deposit security, and banks must have the technology to bring peace of mind to clients. 

“The thing that really hurt SVB is … the depositors lost faith in the viability of the institution,” Will Robinson, chief executive at Encapture, told Bank Automation News. The document management services company works with financial institutions such as Regions Bank, Redstone Federal Credit Union and Prosperity Bank. 

This loss of client trust combined with unnecessarily risky business practices were behind SVB’s failure, Robinson shared in a recent episode of “The Buzz” podcast. 

Even before the bank’s failure, financial institutions, including Wells Fargo, Discover Financial Services, Regions Bank and others, were investing in technology that improved internal controls, decision making, risk management and overall deposit security. 

Now there is renewed focus on how banks are integrating technology to reduce human error and create more secure and trustworthy processes to best service bank clients as regulators keep a close watch.   

Here are four ways banks are doing this: 

1. Intelligent data capture

Banks are looking to intelligent data capture to preserve data integrity, which automatically grabs and extracts data from structured and unstructured documents such as invoices, receipts and forms, according to tech giant Microsoft. 

Wells Fargo is utilizing this technology within its integrated receivables solution, Joe Hussey, global treasury management payments leader at the $1.8 trillion bank, told BAN. 

The technology assists customers and financial institutions in assuring that the extracted data is correct using AI, eliminating the chance of human clerical errors, according to Hussey. 

“The solution captures payment details and remittance data from both electronic payments and lockbox checks they receive from their customers and uses that data to find matches to accounts receivable records in order to create cash posting entries,” he said. “We use AI and advanced data capture to memorize manual corrections made by cash application staff and automatically use the learning in the match process for future payments, as needed.” 

The ability to present data that is accurate and actionable is key to effective decision making for senior bank leadership from the customer experience and risk mitigation perspectives, Chris McGee, managing director of the global financial services consulting practice at AArete, told BAN. 

“Data integrity is so critical in the risk management space, but it’s more critical at the moment, given the heightened environment of risks and uncertainty,” he said. “There’s clearly a correlation and importance in terms of what you’re communicating upwards to help your senior leaders and board understand what’s going on and what can impact them.”  

2. Robotic process automation

Robotic process automation (RPA) allows banks to maintain data integrity as well as other functions, including extracting customer data to prevent fraud and improve experiences for bank employees and customers.  

The $131 billion Discover Financial Services uses the technology for automation of menial tasks, such as data input, which saves significant time and preserves data integrity, Matthew Radaci, principal application engineer at Discover, said at last month’s Bank Automation Summit U.S. 2023 in Charlotte, N.C. 

“What we’re seeing now is internal business teams are less opposed to automation strategy and more willing to adopt it and bring [RPA] on board. They’re more excited,” Radaci said. “They want bots in their environments; they want these remedial tasks removed from their plates so that they can focus on more high-value work.” 

RPA can complete tasks faster while maintaining data quality for reporting purposes, Encapture’s Robinson said. 

“RPA technology can use a digital bot to log into three or four systems, go to the data, pull the data, aggregate it and send it off,” he said. “[RPA] is really focused on efficiency and task reduction, and automating some of the basic data collection and reporting requirements that banks have to do.” 

The $805 billion Bank of Montreal (BMO) uses UiPath’s RPA technology to automate manual workflows, systems navigation, form and document population and data extraction, Nitin Purwar, senior director, banking and financial services industry at UiPath, previously told BAN. 

“We have document understanding, which helps in dealing with structured, semi-structured and unstructured documents in banking and financial services,” Purwar said. 

BMO employs UiPath’s technology for cybersecurity, with the fintech using risk scenarios and rule-based automation to help block potential fraud attempts, leading to reduced operational risk and costs associated with virtual security.  

3. Deposit monitoring

Multibank deposit strategies took the spotlight following the failure of SVB, which led to innovation in the deposit space.  

Tech giant Envestnet, for example, launched its Bank Deposit Index on March 10, one day after the failure of SVB, to cater to the needs of its bank clients, Farouk Ferchichi, president of Envestnet, previously told BAN. The technology allows financial institutions to view deposit flows from other banks that integrate with Envestnet.  

“This is one of those products that is a baby of the [SVB] crisis,” he said of the Bank Deposit Index. “It tracks across the United States the inflows or outflows and net flows of deposits. Then we can break it down by region, state and bank segment.” 

The product allows for tracking of about 15% of households in the United States, 6% of annual banking deposits and 80 million daily transactions across more than 2,000 institutions, according to Envestnet. 

One feature under development for the Bank Deposit Index is alerts based on flow of funds if deposits are higher or lower than expected at the bank using the index as well as the national, regional and state leve, Ferchichi said. 

4. Predictive analytics

Predictive analytics, an emerging technology, can provide financial institutions with potential outcomes to guide business decisions based on information collected from customers and business activities. 

The use of machine learning (ML) helps by allowing for deep data analytics. ML can recognize trends, risks or exposure that might be identified by the human eye, Encapture’s Robinson said. 

Predictive analytics can “find some correlation between certain data points or flag a discouraging deposit trend quicker than a human who is manually reviewing the data,” he said, noting that larger financial institutions likely have access to this type of technology while smaller FIs do not. 

The importance of data within a financial institution cannot be understated, as is ensuring the integrity of data is maintained to glean these insights, AArete’s McGee said. Using predictive analytics can assist in decision making, he added. 

“Using more advanced data analytics tools and being able to leverage those more advanced tools can help identify potential risks quicker and take proactive and preventative steps to address them in a timelier manner,” he said. It can be “applied to transaction monitoring, fraud and financial crime practices, and be more frontward focused than just outside risk, but on the customer experience side as well.” 

Risk management is key

The failure of SVB has proven that banks need to be able to adapt and onboard new clients quickly, and to invest in risk-management strategies, including automating processes where possible, with accuracy.  

Streamlining operations on the front end while mitigating risk efficiently “is where technology plays a huge role,” Robinson said. 

In this episode of “The Buzz,” learn how these four technologies can help FIs manage risk and avoid the pitfalls that led to SVB’s collapse.