The Broken Promise of Auditing Customers Remotely

Why it has crippled lenders – and why it doesn’t have to happen again

by Bakhtier Pulatov, Head of Product at Trade Ledger™

SMEs’ lending needs have long been caught between a rock (corporate banking) and a hard place (independent non-bank lenders). It was never clearer than in lockdown that the current setup doesn’t work – neither for SMEs, nor for lenders.

From paper-based origination processes to manual work, through siloed customer information, the promise of customer-centric lending faces many breaking points that neither party can afford.

We know why SME business finance will never be the same after 2020, but what the lockdown painfully emphasized has a solution that’s both attainable and practical.

Loan monitoring overload brought the lending industry to a halt

A recent internal analysis we carried out shows that lenders are spending up to 45% of their valuable time on loan monitoring and audits.

From collecting information about borrowers from fragmented sources and manually entering it into the system to scoring based on Excel formulas, credit managers’ capability is limited – and prone to human error.

Only 35% of their time goes into the initial credit application analysis and loan approval, with a further 20% invested in loan preparation and onboarding.

During lockdown, lenders that have underinvested in digital loan monitoring and auditing capabilities found themselves in an impasse. In the first half of 2020, as a result of quickly deteriorating portfolio quality, they spent more than 80% of their time on loan monitoring and audits. This strain on resources resulted in high credit rejection rates and – for a lucky few – significantly increased the time-to-cash at a time when companies needed funding the most. The burden on lenders still echoes today, when demand for capital is growing exponentially and many SMEs across the world are nearing the end of their cash runway.

To understand why the industry is overloaded with loan monitoring activities, it’s important to clarify why monitoring is so crucial in our pandemic-stricken world.

Here we look at the factors that contribute to the burdensome nature of monitoring work – especially under lockdown.

Why loan monitoring is so resource-intensive

In our experience, three bottlenecks keep lenders in this self-reinforcing, vicious cycle that drains resources and keeps them from capitalizing on market opportunities.

  1. Collecting and aggregating information on prospective borrowers involves working with fragmented data sources. What’s more, the data can sometimes be low-quality.
  2. Gathering and merging this data frequently entails manual work. Time-consuming, outdated processes put both lenders and borrowers at a massive disadvantage, causing ripple effects throughout the economy.
  3. Credit managers rely on scoring models that don’t account for intangible assets, which an increasing number of SMEs possess. As a consequence, prospective borrowers often get turned down because they can’t use them as collateral – and lack any other tangible assets to utilize.

 

 

Even when this congested process ends with credit approval, the majority of lenders lack the tools and processes to ensure they can smoothly document, monitor, and report portfolio performance. 

These are central challenges for lenders today. But they don’t have to – and shouldn’t – carry this deadweight into the future.

How digital loan monitoring and audit processes minimize time-to-money

Loan monitoring workload increases exponentially when the loan portfolio is not doing well.

During periods of financial turbulence or crisis, lenders focus on ensuring the borrower is financially sound. They monitor and revalue collateral, they update internal risk limits, assess the prospect of covenant breaches, and communicate with financially distressed borrowers.

In effect, it leaves them stuck in this phase of the process that takes up most of their resources, with little room for loan structuring or servicing.

While adding more credit managers and portfolio monitoring officers to the team may seem like the right solution, this is the type of problem hiring more people won’t solve.

Many lenders lack appropriate tools to source relevant credit monitoring and audit information or to generate timely alerts to track the early warning signs of a covenant breach. With no or limited access to meaningful and targeted portfolio analytics, internal coordination and decision-making become substantially more difficult.

Shortening the time-to-cash is not only possible but also achievable with data-driven lending.

We help lenders take advantage of new and growing data sources and automate manual activities, so credit managers can focus on more complex applications, driving more value through their work – and deriving more satisfaction from it.

With end-to-end lending orchestration, lenders aggregate real-time information used in monitoring internal limits and monitor it against the values specified in their credit risk appetite, policies, and procedures. They can also organize and filter this information by product, geography, industry, and quality of portfolios, making it easy to have a global view and to deep-dive into specific areas.

Trade Ledger enables lenders to fully automate monitoring of all covenants which are based on financial ratios calculated from the borrower’s balance sheet, income statement, and cash flow characteristics.

While some information, such as key management changes or acquisitions, will always be monitored manually, we simplify and standardise data collection to remove bottlenecks, lower time/cost-to-serve, and increase both client and staff satisfaction.

 

Source: Moody’s Analytics

 

Achieving operational efficiency

To build an efficient loan monitoring and auditing process, lenders must ensure the workload is adequately calibrated to the purpose. More specifically, monitoring frequency and depth should suit the type and risk profile of the borrower and the type, size, and complexity of the credit facility.

This is difficult to achieve for most credit providers, since manual processes and inconsistent use of data, tools, and benchmarks make it difficult to structure and tailor loans to these characteristics.

Using our end-to-end, fully configurable software platform, lenders easily monitor early warning signs of declining credit quality. We enable them to carry out more frequent and in-depth reviews if our platform identifies a deterioration in the borrower’s credit and asset quality. At the same time, lenders continue to monitor borrowers in good financial standing and free up valuable analytical resources to tackle more complex cases.

 

 

The future of lending is data-driven

The future of SMEs – and the millions of jobs they create (70% of employment, according to the OECD) – also relies on making applying for credit faster, more effective, and more flexible.

Because traditional financial institutions have been slow to digitize their process and integrate new technology and processes, non-bank financial institutions have emerged to capture the opportunity.

“[…] banks face an increasingly dynamic competitive landscape, including the entrance of deep-pocketed alternative nonbank lenders that are using technology to find borrowers and underwrite loans, often using unconventional lending practices.”

To remain competitive and maintain margins, especially under turbulent conditions, lenders must become leaner by adopting technology that streamlines applications and automates the better part of lending decisions.

Post-pandemic lending has to be data-driven lending, as facetime between borrowers and credit providers will decrease substantially. We saw this when the lockdown made it impossible to meet in branch offices, a situation that may reappear in the not-so-distant future. 

To recap, the pillars that enable any lender to prepare for this future of high-efficiency, low-touch relationships with their (prospective) borrowers are: 

  • Tools to automate, corroborate, and structure borrower information from multiple, broader data streams
  • Orchestration of tools and processes to streamline loan structuring and tailor it to specific use cases without the overhead of manual work
  • Automated decision-making by leveraging scalable lending architecture that lowers costs-to-serve and greatly improves user experience – both for customers and employees
  • Ambitious restructuring efforts to match borrowers’ needs in a fast-changing environment. 

 

 

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