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Credit Risk Trends UK 2018

The risk landscape is changing quickly, and new challenges alter how businesses respond to and manage credit risk. Parallel has taken note of how Credit Risk is being shaped by the rise of FinTechs, flexible lending from Machine Learning, challenger banks posing a threat to high street lenders and the inevitable skills shortage manifesting with the evolution of tech and the market.

October 2018

Credit Risk Trends UK 2018


The risk landscape is changing quickly, and new challenges alter how businesses respond to and manage credit risk. Parallel has taken note of how Credit Risk is being shaped by the rise of FinTechs, flexible lending from Machine Learning, challenger banks posing a threat to high street lenders and the inevitable skills shortage manifesting with the evolution of tech and the market. 



The financial services industry is being “reshaped by expanding customer expectations for convenience and personalisation – driven by the bar set by Big Tech firms such as GAFA (Google, Apple, Facebook and Amazon) – combined with FinTechs meeting these expectations with agility and an improved customer journey” according to the World FinTech report 2018 published by Capgemini.


Speed, convenience, flexibility and security are rapidly reshaping the payments landscape, in large part driven by new FinTech start-ups. With FinTech’s ability to use open system, such as APIs and Distributed Ledger Technology, consumers are presented with new and innovative ways to manage and spend their money.


Open banking has also led to a shift from existing payment platforms – offering consumers more convenience, agility and personalised banking options. Individuals and businesses alike are using apps developed on the FinTech landscape.  Ash Gupta, former Chief Risk Officer and President of Global Credit Risk for American Express, notes, “Fintech and digital lenders have created new excellence in customer experience and have permanently changed customer expectations with regards to speed of credit decisioning and the choice and flexibility available with regards to range of offers and prices”.


What does this mean for the Credit Risk market?

The high street banks are no longer the only option when you are borrowing. Alternative lenders give people more options and the ability to borrow. The larger banks are having to adapt their way of working and looking at the market. People now have access to real time spending data and can do all their banking entirely on their phone.  



There have been considerable advances in the application of Machine Learning recently.  This is due to the variety of industry motivators that have revolutionised the use of these techniques in the risk management sphere, and beyond. So, can Machine Learning improve flexibility in lending?


In lending decisions, machine learning can produce fantastic value when applied to nonlinear problems where there is a tremendous amount of data, specifically unstructured data. As organisations’ lending processes grow to include new ways of capturing data (i.e. from online applications and social media forms), the use cases for incorporating machine learning techniques will continue increase.


In terms of credit risk, organisations can use clustering algorithms to understand how a consumer’s credit history and transaction profile has changed over time. This information can then provide the lending institution an enhanced credit decision making process for both new and existing customers. Machine Learning can also be used to enhance feature selection to improve credit scoring.


Machine learning can improve the precision of all predictive models used in consumer lending, such as a consumer’s probability in responding, approval, deliquency, default and other behaviors. Improved models are a critical factor in perfecting decision-making, allowing lenders to maximise revenue while holding credit losses below the specified threshold and, therefore, optimising the net income line (credit management). With the role of consumer lending growing as an attraction to new customers and a way to build long-term consumer loyalty, machine learning will stand to be implemented at a higher rate to meet increasing revenue targets whilst reducing lenders’ credit risk.


What does this mean for the Credit Risk market?

Alternative Credit Scoring is allowing businesses to make more informed decision on their lending strategy. This means that more people have access to Credit and businesses can assess you on more than just a scorecard.  Businesses are beginning to use Data Science techniques more in a risk environment. The analytics remains the same fundamentally, but the purpose of the analytics changes.

“The market is more buoyant than ever. New ways of lending and more accessible credit, means that analytics is more vital than ever before. Businesses are always looking for the best analytical minds to ensure they cover their risk, while offering the best service on the market they can, to the widest possible range of customers.” says Steven Harrison, our leading Credit Risk Specialist.




UK Challenger banks have gained traction over the past few years by making retail bank services available on your mobile device. Europe has seen the first wave of challenger banks, such as Atom Bank, Tandem Bank, Monzo, Starling Bank, Revolut and N26.


* Infographics by CBInsights


The UK has seen the most challenger bank activity in comparison to other regions due to progressive regulations put in place to enhance competition and breakup the banking monopoly. The UK’s open banking standards and the EU’s Revised Payments Services Directive (PSD2) give challenger banks the space to grow. Open banking and PSD2 enable third parties to safely and securely access customers’ current account data. This means that there is also a big opportunity for more FinTechs to get into traditional banks and build new services for consumers.


What does this mean for the Credit Risk market?

Exciting new challenger banks aren’t just changing the high street. They’re changing the landscape of the analytical job market. They offer the opportunity to do analytics that may not be possible in High Street Banks and with more scope, as the teams are smaller and less specialised. There are now more options for candidates, in the banking space, than ever before.  Open source tech is becoming more important. Smaller, up and coming businesses will use free software for their analytics.




The rise of FinTechs, Data Science tech being deployed for enhanced flexibility in lending, and challenger banks offering alternatives to high street lenders are all contributing to a greater skills shortage within the UK.


Finding Credit Risk professionals with a Data Science background is no easy feat. Professionals who have the Data Science skills coupled with the Financial Services experience sought after by employers is quite difficult to attract to London FinServ firms.


As the FinTech sector grows, so does the demand for professionals within the market. Alongside this rapid growth of FinTech firms, the sector is expected to top 100,000 employees by the year 2030, producing 30,000 new jobs. At the present, UK FinTech firms are highly dependent on global talent, with 42% of employees coming from outside the UK and 28% of these employees from the European Economic Area.  If a flexible immigration policy is not maintained, the UK FinTech sector could lose up to £361m due to a shortfall of highly-skilled workers and inability to present the UK as an attractive place to do business.


Access to a global talent pool and presenting the UK as an attractive place to work are two perennial issues affecting all sectors of the economy, specifically that of the FinTech sector. These issues are not necessarily all due to Brexit, but Brexit highlights the challenges facing the market in the future.


Without acquiring a flexible approach, the UK FinTech sector stands to lose its global standing with FinTech companies that are already facing challenges in recruiting those with the essential skills and talent. 


Access to a Global Talent Pool with the appropriate skillset is possible by partnering with a company like Parallel. With our extensive network stemming back over 15 years, access to global talent and personalised employee value propositions created for our clients, Parallel is perfectly positioned to ensure our clients don’t fall victim to the challenges facing the market.