The Commercial Lending & the Way Forward
Prior to joining OakNorth, Seam was one of the first commercial engineers at Palantir Technologies, and had also worked as a strategist at Goldman Sachs.
siliconindia questions Sean Hunter on how the industry is working towards strengthening SMEs using technology.
1. Why are customised lending solutions for SMEs gaining importance?
Providing SMEs with customized lending solutions is something that has always been important, it’s just not always been possible from an economics perspective.
If you are an individual or a small or medium-sized business aiming to borrow a few thousand to a few hundred thousand dollars, technology has made it possible for lenders to provide an extremely efficient service and for you to get a near-instantaneous credit decision.
For larger or more complex loans however (i.e. loans of $1 million to $25 million) it remains a fundamentally manual and expensive process. A bank given the choice of issuing a loan of $100 million with a one percent arrangement fee, delivering $1 million in fees, or issuing a loan of $10 million with a one percent arrangement fee, delivering $100,000 in fees, will naturally focus on the bigger deal, meaning the likelihood of the smaller loan getting a customised facility is not very high. In the mid-market, where loans are too big for automated decision models but too small for the unit economics of the manual approach to make commercial sense, the market has been characterised by fairly inflexible, product-centric lending which does not necessarily meet borrowers' needs.
Our idea therefore, was to leverage new technologies (machine learning, massive data sets and cloud computing) to undertake the type of forward-looking credit analysis and monitoring that normally only occurs with bigger ticket corporate lending and apply it to medium-sized businesses (the Missing Middle). By combining these technologies with specialist credit teams, we would be able to provide businesses with a fundamentally better borrowing experience both in terms of speed and getting a facility that’s structured to their needs, at a lower cost, unimaginable to the big banks.
2. What role does AI play in providing customised debt financing?
While a fully-automated decision process will probably never be appropriate for loans in the millions of dollars, artificial intelligence and large-scale data analysis can help bridge the gap between fully automated and fully manual credit assessment, allowing an efficient, semi-automated process while still preserving some of the customisation necessary to address the complex needs of small and medium-sized businesses.
3. Do you think automation will pose a threat to human-based credit analysis?
At OakNorth, we’re firm believers that humans will always be a vital part of financial services and more specifically, the commercial lending process and credit analysis. While there is not enough data to produce and fit a general model that would accurately assess all corporate credit analysis cases in this class, to perform this task in a fully manual fashion requires the credit analyst to perform a very large number of tasks – some of which can be automated given machine learning techniques applied to the data we do have.
This hybrid approach is a pragmatic compromise where computers perform various tasks to allow the analyst to be more efficient, but the analyst remains in the driving seat and is able to train the models and direct and shape the final outputs to ensure they are coherent and understandable. This also means that it is not necessary to solve the entire suite of problems before automation is of some help to overall efficiency, with analysts plugging gaps in the process that are not yet automated.
4. What are the current shortcomings/challenges in the commercial lending sector?
When it comes to commercial lending, banks rely on risk models to make decisions. These models have been built up internally over decades of lending across thousands, if not tens of thousands of loans, but COVID-19 has exposed unexpected shortcomings in them.
The first issue is that these models are based on historic data which doesn’t adequately reflect the unique situation we now find ourselves in or take into account the future challenges that the world will be facing as it enters into the worst recession in three centuries. The second issue is that they make broad assumptions about entire sectors rather than developing an understanding of the portfolio at the granular loan level and taking into account the individuality of each business. The third and final issue is that these models don’t take into account how quickly the situation is changing.
COVID-19 has been a catalyst to help overcome these shortcomings, with lenders now realising that they need to course-correct and take a fundamentally different approach to commercial lending than what’s been done for decades.
We believe that in the future, banks are going to have to:
• Combine backward-looking data with a forward-look view
• Take a granular, loan-by-loan approach rather than an overall portfolio or sector-level approach to credit analysis
• Conduct reviews on an ongoing basis, rather than annually
• Update parameters to reflect the ever-changing situation
• And use alternative data such as foot traffic to inform models
5. How do you think fintech has evolved over the last few decades?
According to KPMG,, the global fintech sector has experienced a boom in recent years, attracting almost half a trillion dollars of investment since 2015. So called ‘neo-banks’ or ‘challenger banks’ are responsible for a significant amount of this, with several having closed nine-figure rounds in the last 24 months. The focus for many however – both on the investor and investee side – has been on growth, with profits being viewed as more of a nice to have than a need to have.
However, COVID-19 is changing this and putting profits firmly on the agenda. The fintechs who can get to profitability faster whilst still achieving ambitious growth targets, are therefore likely to be the biggest beneficiaries of investment going forward.
6. Given the current pandemic, how is the commercial lending industry coping?
Historically, banks and commercial lenders have relied on a small number of monolithic suppliers and systems to provide them with broad capabilities, augmenting their own internal development, to provide all their infrastructure. These systems are patched to add features as banks grow and markets evolve. Mergers can lead to overlapping, incompatible systems; the bank’s infrastructure can make these systems brittle, costly and time-consuming to change.
COVID-19 and the subsequent government interventions, however, are forcing banks to move quickly: multi-year projects would never adequately address the emergency needs of customers and existential challenges of businesses. The crisis comes at a seminal moment for the industry, when many banks are beginning to experiment with cloud infrastructure. These solutions are able to provision (or decommission) infrastructure in seconds what previously would have taken years, and are well suited for rapid experimentation.
The extraordinary circumstances brought about by the pandemic have led to a moment of unique opportunity for both banks and fintechs. The economic environment and policy responses by the federal government has meant that banks are forced to act with surprising resourcefulness and agility. They are now seeking to carry this momentum to radically transform projects that seemed previously destined to move at a snail's pace.
To do this at speed and at scale, they have had to look beyond the short list of traditional vendors and implementation partners more accustomed to project timelines of several years, to a constellation of smaller, more agile fintechs that are able to meet specific needs at a rapid pace. The Davids and Goliaths are finally working together — so far, the outcomes have been pretty phenomenal.
siliconindia questions Sean Hunter on how the industry is working towards strengthening SMEs using technology.
1. Why are customised lending solutions for SMEs gaining importance?
Providing SMEs with customized lending solutions is something that has always been important, it’s just not always been possible from an economics perspective.
If you are an individual or a small or medium-sized business aiming to borrow a few thousand to a few hundred thousand dollars, technology has made it possible for lenders to provide an extremely efficient service and for you to get a near-instantaneous credit decision.
For larger or more complex loans however (i.e. loans of $1 million to $25 million) it remains a fundamentally manual and expensive process. A bank given the choice of issuing a loan of $100 million with a one percent arrangement fee, delivering $1 million in fees, or issuing a loan of $10 million with a one percent arrangement fee, delivering $100,000 in fees, will naturally focus on the bigger deal, meaning the likelihood of the smaller loan getting a customised facility is not very high. In the mid-market, where loans are too big for automated decision models but too small for the unit economics of the manual approach to make commercial sense, the market has been characterised by fairly inflexible, product-centric lending which does not necessarily meet borrowers' needs.
Our idea therefore, was to leverage new technologies (machine learning, massive data sets and cloud computing) to undertake the type of forward-looking credit analysis and monitoring that normally only occurs with bigger ticket corporate lending and apply it to medium-sized businesses (the Missing Middle). By combining these technologies with specialist credit teams, we would be able to provide businesses with a fundamentally better borrowing experience both in terms of speed and getting a facility that’s structured to their needs, at a lower cost, unimaginable to the big banks.
Mergers can lead to overlapping, incompatible systems; the bank’s infrastructure can make these systems brittle, costly and time-consuming to change
2. What role does AI play in providing customised debt financing?
While a fully-automated decision process will probably never be appropriate for loans in the millions of dollars, artificial intelligence and large-scale data analysis can help bridge the gap between fully automated and fully manual credit assessment, allowing an efficient, semi-automated process while still preserving some of the customisation necessary to address the complex needs of small and medium-sized businesses.
3. Do you think automation will pose a threat to human-based credit analysis?
At OakNorth, we’re firm believers that humans will always be a vital part of financial services and more specifically, the commercial lending process and credit analysis. While there is not enough data to produce and fit a general model that would accurately assess all corporate credit analysis cases in this class, to perform this task in a fully manual fashion requires the credit analyst to perform a very large number of tasks – some of which can be automated given machine learning techniques applied to the data we do have.
This hybrid approach is a pragmatic compromise where computers perform various tasks to allow the analyst to be more efficient, but the analyst remains in the driving seat and is able to train the models and direct and shape the final outputs to ensure they are coherent and understandable. This also means that it is not necessary to solve the entire suite of problems before automation is of some help to overall efficiency, with analysts plugging gaps in the process that are not yet automated.
4. What are the current shortcomings/challenges in the commercial lending sector?
When it comes to commercial lending, banks rely on risk models to make decisions. These models have been built up internally over decades of lending across thousands, if not tens of thousands of loans, but COVID-19 has exposed unexpected shortcomings in them.
The first issue is that these models are based on historic data which doesn’t adequately reflect the unique situation we now find ourselves in or take into account the future challenges that the world will be facing as it enters into the worst recession in three centuries. The second issue is that they make broad assumptions about entire sectors rather than developing an understanding of the portfolio at the granular loan level and taking into account the individuality of each business. The third and final issue is that these models don’t take into account how quickly the situation is changing.
COVID-19 has been a catalyst to help overcome these shortcomings, with lenders now realising that they need to course-correct and take a fundamentally different approach to commercial lending than what’s been done for decades.
We believe that in the future, banks are going to have to:
• Combine backward-looking data with a forward-look view
• Take a granular, loan-by-loan approach rather than an overall portfolio or sector-level approach to credit analysis
• Conduct reviews on an ongoing basis, rather than annually
• Update parameters to reflect the ever-changing situation
• And use alternative data such as foot traffic to inform models
5. How do you think fintech has evolved over the last few decades?
According to KPMG,, the global fintech sector has experienced a boom in recent years, attracting almost half a trillion dollars of investment since 2015. So called ‘neo-banks’ or ‘challenger banks’ are responsible for a significant amount of this, with several having closed nine-figure rounds in the last 24 months. The focus for many however – both on the investor and investee side – has been on growth, with profits being viewed as more of a nice to have than a need to have.
However, COVID-19 is changing this and putting profits firmly on the agenda. The fintechs who can get to profitability faster whilst still achieving ambitious growth targets, are therefore likely to be the biggest beneficiaries of investment going forward.
6. Given the current pandemic, how is the commercial lending industry coping?
Historically, banks and commercial lenders have relied on a small number of monolithic suppliers and systems to provide them with broad capabilities, augmenting their own internal development, to provide all their infrastructure. These systems are patched to add features as banks grow and markets evolve. Mergers can lead to overlapping, incompatible systems; the bank’s infrastructure can make these systems brittle, costly and time-consuming to change.
COVID-19 and the subsequent government interventions, however, are forcing banks to move quickly: multi-year projects would never adequately address the emergency needs of customers and existential challenges of businesses. The crisis comes at a seminal moment for the industry, when many banks are beginning to experiment with cloud infrastructure. These solutions are able to provision (or decommission) infrastructure in seconds what previously would have taken years, and are well suited for rapid experimentation.
The extraordinary circumstances brought about by the pandemic have led to a moment of unique opportunity for both banks and fintechs. The economic environment and policy responses by the federal government has meant that banks are forced to act with surprising resourcefulness and agility. They are now seeking to carry this momentum to radically transform projects that seemed previously destined to move at a snail's pace.
To do this at speed and at scale, they have had to look beyond the short list of traditional vendors and implementation partners more accustomed to project timelines of several years, to a constellation of smaller, more agile fintechs that are able to meet specific needs at a rapid pace. The Davids and Goliaths are finally working together — so far, the outcomes have been pretty phenomenal.
