Lending to SMEs within 24 hours

Bruce Brenkus
Bruce Brenkus

Olena Gryniuk talked to Bruce Brenkus, Chief Risk Officer, about Spotcap’s ability to make sound credit decisions in less than 24 hours.

During recent years Spotcap entered 2 new markets, which are the UK and New Zealand. How do you estimate the competitive landscape there? Especially the UK market which is perceived to be one of the most competitive, being a European centre of fintech?

This is an interesting question, because, of course, we are dealing with two very different landscapes, New Zealand has a very little competition and as you mentioned, the United Kingdom has several fintech and other players in the market. The short answer is we have embraced both scenarios. In the UK for example, we are quite happy to have competitors as they have helped the market get to a point where SMEs understand what fintech is, they recognise it as a new lending opportunity. Our Bruce Brenkus, Chief Risk Officer at Spotcap job is to provide the best possible product and service to our SME customers. On the other end of the spectrum, and on the other side of the world in New Zealand, our job is to build industry awareness and educate potential customers about the valuable service we provide. We have developed individual strategies to embrace these diverse markets.

What’s the range of your loans?

We start as low as 10,000 in local currency whether that’s euros, pounds or dollars (AUD or NZD) and we go up to 250,000 local currency in each market.

Who is your average customer? How does he look like: age, industry, company’s size?

There is an interesting assumption that fintechs or alternative lenders are primarily used by early adopters. Although I can’t speak for every fintech, our average customer is over forty, has been in business for somewhere between 5 and 7 years and enjoys an annual turnover of £1-10 million (in the UK). Spotcap’s reach beyond the early adopter is
largely powered by the partnerships we have made with traditional lenders, brokers, accountants and advisors.

Coming back to your risk model and lending process. How does it look
inside Spotcap? So, it takes maximum 24 hours to make a decision, isn’t it? How it goes from the step of receiving an application from the customer till loan disbursement?

We’re able to provide a 24-hour turnaround from the time we have received a completed application which is great for our SME customers with an immediate need for finance. Once we receive an application we undertake qualitative and quantitative analysis. Our risk models provide an initial recommendation about the interest rate and the size of the credit line, however the final decision is always made by one of our expert underwriters. We use advanced platforms to do a lot of the pre-work and make predecisions then we let our very smart, qualified underwriters take it from there, because no model can cover every aspect of a lending opportunity.


So, this was my next question what is the human input into your credit underwriting process?

We have already developed auto decision rules for a portion of our applications with the goal to fully automate credit decision processes soon. However, even with a fully automated credit decision process, we will still rely on our skilled credit operations team to provide many of the final credit decisions. Let me give you an example – let’s say we receive a small loan request, say 25 thousand in local currency, the automation in the tools may take it all the way to a final decision, so much so that our underwriter only has to validate and verify, almost like a quality control check. On bigger loan applications, the model will only provide a base recommendation. We use the red light, yellow light and green light approach which means our models will place an application in one of those buckets and the underwriting team takes it from there. We also have a secondary level of authority within the business – we call it ‘four eyes’ – to make sure every decision uses sound logic and best practice underwriting.


Tell us more about the technical part of the process. How do you utilize machine learning within underwriting and the whole risk assessment process?

We utilize machine learning in what we call in an R&D laboratory environment. Spotcap Labs is our center where we work on the next generation of credit scoring to find predictive patterns for credit decision processes. We collect big data to develop our scoring algorithms. The lab uses our vast array of data and various techniques including machine learning to improve our credit decision scorecards, segmentation strategies, and product features such as pricing and credit line size determination.

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We are not always trying to look at building a new model but we are trying to identify new data attributes that can be a combination of attributes from certain of our data streams and data vendors. We are also looking at different data vendors to find out if new data sources have any added value. I love the phrase, and I hate the phrase ‘Big Data’ because having a ton of data can slow you down. So, what we try to do is wade through it to find the data which is actually helpful. And finally, we do a lot of text mining. Lots of businesses overlook how important the written word can be for data mining. We have made this one of our core focus areas.

Nowadays fintech lenders set a high customer-service bar for banks and present new challenges for their risk functions. So, as I understand, you do not require a big package of documents from the customer and long loan applicants. So, do you draw some data from public sources like PayPal transactions, Amazon, etc.? How do you do this in Spotcap?

To clarify, we do require certain documents from our clients – that’s a crucial part of our sound underwriting, and authentication process. But you are correct that we do not ask for unnecessary documents. We ask for financial statements, bank account data, credit reports and taxation information. So, using all those different tools we do a lot of cross-checking and verification: do the bank account inflows and outflows match up with revenue? Are there any discrepancies within the data? Our algorithm analyses all of this data and considers KYC (Know-Your-Customer) checks to make sure that we have the right business, the right owners or directors and we are able to verify that is a legitimate business with an active business licence.

Bruce Brenkus
Bruce Brenkus

What is loan approval rate in Spotcap? Is it growing? What is your targeted one?

We aren’t too focused on a number here, because it depends of the applications we receive but we have found we are approving around half of our applications, which of course, is more than the traditional players because of our unique credit algorithm. We do a lot of business through partnerships which is a lot more controlled and constrained because our partners are trained to identify a robust loan candidate. Our main goal when we look at approval rates is that we are approving profitable customers with longevity in their business models.

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Bruce, what does your everyday job looks like? What takes your maximum involvement now?

My day starts early in Berlin, I get involved with some of the final decisions on larger tickets so there is a lot of interaction with the underwriters to make sure we are constantly developing our skills and expanding our knowledge base. But most of my job happens in risk management. For example, I look at how the portfolio is performing, what’s going on, what’s working, what’s not working, working with the decision science and the modelling team to find new techniques, developing models and of course, the normal strategic things to from pricing, to liaising with investors.


Bruce, what is your personal source of inspiration for doing a better job?

I love the phrase “continuous and never ending improvement” I’ve been using it for years. It’s important because in this industry you always must continue learning because the environment and small businesses are changing. You should always look forward. As a risk person, you must be very balanced, it’s easy to take zero risk and get zero losses but that means you are putting zero on the books, and that doesn’t work. So, my philosophy is always finding the right balance between profitability. Once you put a model together we celebrate as a team, we high five, we cheer than five minutes later I’m trying to get the team to start the road to beat the new model. The philosophy I’ve always had is to put things into actions, learn and try new things but we don’t want to end up in what I call ‘analysis – paralysis’, we find things that make sense and put them in action and if there are certain things that don’t work we move forward and look at other things.

The companies that are the most fascinating for us are really in other industries that are using the technologies in decision sciences in different ways, so medical research do a lot of in anomaly detection, sensory type of models that is being used in driverless cars like Uber and Google are ones we are looking at to understand. Face recognition is also interesting for us from a security and identification perspective. Although these technologies don’t have an obvious connection to what we do we are always looking for ways to constantly develop and improve and often that means looking outside the box.

Spotcap is an online lender to small and medium-sized businesses. The company was founded in 2014 and is led by Founder and CEO Dr. Jens Woloszczak. Spotcap now operates in five countries and has secured more than EUR 78 million in funding. The business has enabled thousands of SMEs to innovate, remain competitive and grow. Spotcap is backed by world-class investors including, Rocket Internet, Access Industries, Holtzbrinck Ventures, Kreos Capital, Finstar Financial Group and Heartland Bank. Spotcap employs more than 110 people globally and is headquartered in Berlin (Germany) with a local presence in Spain, the UK, the Netherlands, Australia and New Zealand. Olena Gryniuk


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