How Will New Financial Risk Regulations Affect European Banks?

Author: Amanda Fridlund
Posting date: 8/8/2019 8:46 AM
The financial crisis of 2007-2008 changed banking. The world moved from taking mortgage loans in our dogs’ names to introducing strict regulations for banks prohibiting them from giving out loans to “anyone” without assessing Risk properly. In 2010 the Basel Committee on Banking Supervision (BCBS) introduced BASEL III, a regulatory framework that builds on BASEL I, and BASEL II. This framework changed how banks and financial institutions asses risk. It introduced an Advanced Internal Rate Based Approach (Commonly known as the AIRB approach). 

Now, the committee has introduced new changes and, by 2022, all banks and institutions will have to implement the revised IRB Framework, as well as new revised regulations for the standardised approach, CVA Framework and new frameworks for Operational Risk and Market Risk. So, what does this mean for those working Risk?

Change Is Coming


Change is inevitable, no matter what you do. If you work in Risk Management and Compliance, change is something you can expect to happen, often. As mentioned above, by 2022 there will be lots of changes. The Basel Committee calls this initiative the “finalised reforms”, or BASEL IV which builds on the current regulatory framework BASEL III. Quickly summarised, the changes limit the reduction in capital that effect banks IRB models. 

This change is predicted to impact banks in Sweden and Denmark the most, with estimations that capital ratio will fall by 2.5-3%, far higher than the 0.9% expected for the average European bank. 

So what does all this mean for Swedish and Danish banks? 

What’s Happening Now?


One of the main things that Swedish and Danish banks need to revise for these new regulations, are their internal models. The new regulations introduced a new definition of Probability of Default, measured through a model commonly known as a PD model. Effectively this means that every bank must “re-develop” their internal PD Models in the IRB approach. Consequently, we are already seeing a clear response from the banks in their strategies moving forward.

It has already become quite apparent that many banks are looking to make IRB model development their focus for 2019-2020 and 2021. This has resulted in a boom in the hiring space for developers with experience in IRB Modelling and Credit Risk Modelling in general, which in turn has led to high demand in the face of the low supply of these types of candidates. Understandably aware of this, modellers are now looking to negotiate higher salaries. 

What You Can Do 


For candidates that hold the right experience, there are good opportunities at hand. If so inclined, they can utilise this chance to finally see if the grass actually is greener on the other side, or not. However, there are a couple of things worth considering before making a move.  

Firstly, are you actually keen on switching jobs? Your skills are probably equally in demand at your current employer and, if you are having doubts about moving from the get-go, you may well be able to negotiate a rise without pursuing a new opportunity. However, if you are serious about finding something new, this is a great time to do so. The majority of banks have found that these new regulations are creating an unsustainable workload,  and are now looking for talent externally to expand their teams. This means that the experienced modeller can pretty much have their pick of the litter. 

Furthermore, if you are a junior modeller, there are now plenty of opportunities for you to enter a niche area known for being exciting and innovative. So, wherever you are in your career, these regulatory changes  are likely to have a large impact and open up new avenues for you to explore.  

We all know that regulations in banking and finance are now essential, we all agree, even if they can be a little frustrating. However, what people often fail to think of are the opportunities new regulatory requirements create. In the case of BASEL IV, we’re already seeing an increase in demand for strong talent, and a demand for people who are passionate about Risk Management and model development. 

For businesses, new regulations also provide the chance to not only improve their teams, but to  create new models that can be utilised to optimise and automate. A lot of financial institutions are already aware of this and are using these models to gain competitive advantage over their competitors, as well as to stay one hundred percent compliant. 

If you’re looking to build out you Risk Management team or take on a new Risk opportunity for yourself, we may be able to help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more. 

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Mitigating Risk In The Financial Services Sector With Machine Learning

Data & Analytics is an industry that is constantly evolving and is always using the latest technology to innovate its services and capabilities. More recently, these advancements have moved in areas such as Artificial Intelligence (AI) and Machine Learning (ML). Machine Learning is a method of data analysis, under the branch of Artificial Intelligence, that allows systems to learn from data, identify patterns and ultimately make decisions with little to no human intervention. Used across a vast range of sectors, this arm of Data & Analytics has become widely popular, especially within highly-advanced industries such as Finance.  Since the 2008 financial crash, at the top of the agenda for many Financial Institutions (FIs) was, and still is, the need to protect business, increase profitability and, possibly most importantly, address the abundant inadequacies of risk management. This includes risks posed by consumers such as liquidity, insolvency, model and sovereign, as well as any internal process and operational risk the FIs may also be facing through any failures or glitches.   Machine Learning has played a crucial role in improving the quality and precision of FIs risk management abilities. In HPC Wire, it has been reported that the use of AI and ML within the financial sphere to mitigate risk, improve insights and develop new offerings may generate more than $250 billion for the banking industry.  How does ML work? By using incredibly large data sets, drawn (with consent) from consumers, ML can learn, and predict, patterns in consumer behaviour. This can be done in one of two ways: through supervised learning tools, or unsupervised learning tools.  Explained by Aziz and Dowling; “In supervised learning you have input data that you wish to test to determine an output. In unsupervised learning, you only have input data and wish to learn more about the structure of the data.” How do banks use ML to mitigate risk? In FIs, a mix of the two ML tools are used. Most commonly, we can expect to see learning systems such as data mining, neural networks and business rules management systems in play across a lot of banks. These models work in tandem to identify relationships between the data given from the FIs and their consumers – from their profiles to their spending habits, credit card applications to recorded phone calls – which then build ‘character profiles’ of each individual customer. The process can then begin, spotting signs of potential risk factors. This may include debt, fraud and/or money laundering.  Here we break down two key examples.  Fraud Thanks to ML, customers have become accustomed to incredibly quick and effective notification of fraudulent activity from their banks. This ability from FIs comes from large and historical datasets of credit card transactions and machines which have been algorithmically trained to understand and spot problematic activity. As stated by Bart van Liebergen; “The historical transaction datasets showcase a wide variety of pre-determined features of fraud, which distinguish normal card usage from fraudulent card usage, ranging from features from transactions, the card holder, or from transaction history.”  For example, if your usual ‘character profile’ is known by ML tools to spend between £500 - £1000 per month on your credit card, and suddenly this limit is overtly exceeded, fraudulent activity tags will be alerted, and the freezing of your account can be done in real-time.  Credit applications When borrowing from a bank or any other FI, consumers must undertake a credit risk assessment to ensure that they have a record of paying back debt on time, and therefore not adding greater cost, and risk, to the lender.  Traditionally, FIs have approached credit risk with linear, logit and probit regressions but, serious flaws were found in these methods, with many applications being left incomplete. In this space, the evidence for the effectiveness of ML is overwhelming. Khandani et al. found that FIs using ML to analyse and review credit risk can lead to a 25 per cent cost saving for the FI involved.   These ML models come in various shapes and sizes, with the most common being instantaneous apps or websites which allow users and their banks to have access to real-time scoring, data visualisation tools and business intelligence tools.  The risk of risk management with ML Like with any AI or ML application or tool, there will always be cause for concern and real need to always remain vigilant. While ML has shown to be an invaluable tool across lower risk areas, the complexity of more statistical areas of banking, such as loans, has proven to be an Achilles heel for the technology. This usually stems from bias, a perpetual problem for AI and ML across all industries.  Technology Review notes that “There are two main ways that bias shows up in training data: either the data you collect is unrepresentative of reality, or it reflects existing prejudices.”  Data, analytics, AI and ML are notoriously non-diverse working sectors. The person behind the screen creating learning algorithms tends to be white and male, and very unrepresentative of the whole society that the machine learning tool will serve. Over the years, we have heard numerous accounts of unfair and unjust machines, which have learned from a very narrow and unrepresentative dataset, which stems from the lack of diversity amongst the employees within the Data & Analytics industry. For example, Microsoft’s racist bot and Amazon’s sexist recruitment tool, both clear examples that ML and AI are not ready to be used on their own, and humans still need to play an integral part in decision making.  Banks and FIs must be aware of the, potentially lethal, consequences that bias in ML may present. Lenders must be careful to ensure they are working within the guidelines of fair lending laws and that no one group of people are being penalised for no reason other than issues within the technology and its algorithms. It is vital that the humans behind the technology don’t rely on ML to provide them with an answer 100 per cent of the time but, instead, use it to aid them in their decision making when it comes to risk mitigation.  If you’re looking for a role in Data & Analytics or are interested in finance or Risk Analytics, we may have a role for you. Take a look at our latest opportunities or get in touch with one of our expert consultants to learn more.  

Risk Analytics & Your Job Search

Risk Analysis is a daily part of our decision-making process. Its influences are felt in how we prioritise projects. Hello, project management. It determines when we take breaks. And in the working from home or remote working culture, lines between personal and professional life are soon blurred. Our decision-making ratchets through microanalyses of next steps, words to use, when to log off the computer, and what time to make lunch or dinner. Almost without realising it, we're using the SWOT matrix determine risk. That's just in our personal day-to-day lives. Organisations have been using these strategic planning techniques as a daily part of their enterprise lives. So, imagine this. You determine your strengths, weaknesses, opportunities, and threats (SWOT) to write your CV, land the interview, and get the job. Aren't you already on your way to helping your company determine their risk on a bigger scale?  Isn't Risk Analysis the Same as SWOT? Not exactly. While you are identifying, measuring, and analysing issues, the idea is to avoid or lessen risk. In the movie War Games, the main character decides he's going to play a game. But, neither the computer nor the main character understands the game is not a game. They have no idea what risk they've unleashed as keystrokes begin to lead to war.  Militaries use Risk Analysis regularly to determine if war should begin, estimated casualties and cost just for a start. But, in the end, it's usually decided peace keeps the world away from war after all the risk has been analysed. Though most businesses and individuals don't have the risk of war at their doorstep, there are extenuating circumstances which must be determined to avoid risk. This can be anything from natural disasters to legislation to physical requirements and locations. The SWOT technique is a tool within strategic planning as are Risk Analytics. Let's take a look at the benefits of Risk Analysis.  It can help your business improve security, manage costs, and plan for any surprises. Whether it helps you in your job search or manage your business once you're hired, a combination of SWOT and Risk Analytics can help your decision-making process shine. Done well, Risk Analysis is an important tool for managing costs associated with risks, as well as for aiding an organisation's decision-making process. SWOT your way to a Successful Job Search So, first, let's take a look at what SWOT is and isn't. This type of analysis is a tool most often found in strategic planning for organisations. But, it's not the only tool when assessing risk. Like any data profession, you'll want to gather, collect, and analyse the information before you. And when it comes to the job search, knowing yourself and your self-awareness levels may play a bigger part than you imagine. Skipping ahead to the interview from your 'foot in the door' CV and cover letter both delivered via video, of course, you begin to plan for your interview. "What are your biggest strengths and weaknesses?" your hiring manager asks. You've come well-prepared for this question because you've done your SWOT analysis. From the moment you decided your areas of expertise, your roles in organisations, and any areas you know you need to improve but can turn into a positive. You've pre-assessed your strengths, weaknesses, opportunities or obstacles, and threats. Strengths – What characteristics do you possess which gives you an advantage over your competition? Have you cross-trained across a variety of departments? Do you have a knack for telling a compelling data narrative story to help make leadership make an informed decision? Weaknesses – Where could you use more training? Are you looking for a business that offers it in-house or do you need a certification or class to give you a leg up over your competition? What puts you at a disadvantage and how would find a work around? What can you do to improve? Opportunities or Obstacles – Has your experience taught you a new way of doing things within the industry? Does your experience extend from working alone to strong member of a team? Was the team in-house or scattered around the world? How did it affect your working style and what did you learn from it? Threats – Threats may seem at odds with a job search, but…that was then. This is now. Threats are simply external forces which cause trouble in your planning. In other words, this is your risk assessment. What risks are involved and of those risks which will have less effect and which will have more on your desired outcome? Performing this technique in your personal and professional life helps you peel back the layers of you. What has your education prepared you for? Your work experience? The projects you've chosen or been assigned to? It's all leading somewhere, right? This is where you match your strengths to opportunities. Hello, dream job. If you're looking for your next role in the Data & Analytics, we may have a job for you. Take a look at our current opportunities or get in touch with one of our expert consultants to learn more.  

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