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How To Get Ahead In Risk Analytics

Ewan Dunbar our consultant managing the role
Author: Ewan Dunbar
Posting date: 6/26/2019 3:36 PM
In the world of Risk Management, top talent is always in high demand. Despite this, those who specialise in area know that progression can be difficult and, according to our 2019 Salary Survey, is the slowest in the industry. So, how can you differentiate yourself from the competition, and what steps can you take to make yourself the ideal candidate for that promotion or new job you’ve been hunting? 

Whether you are looking to move somewhere new, or trying to climb the ladder in your current company, here are some ways that you can make sure you stand out. 

Stay one step ahead in tech


Traditionally, the Risk Analytics tech stack has comprised of SAS, SQL and VBA. SAS and SQL remain very much present, but we are also seeing a clear increase in the use of Open Source programming languages, such as R and Python. Unsurprisingly, a lot of Risk Modellers and Analysts are now spending their time in developing their skills in these languages. One might argue that if you know one language, there’s not too much work required to upskill in another when you take on a new role, but this isn’t necessarily true. By being proactive and evolving your skillset in your current position, your CV will have a much bigger impact when it lands on a Hiring Manager’s desk. 

Over the past few years, we’ve also seen the arrival of Machine Learning and AI in the world of Risk. Whilst many businesses are still slow to embrace these technologies, do not be surprised to see them make a big impact over the next couple of years. Risk Analytics are catching up to the rest of the industry in regards to technology, and having the knowledge and skillsets required in these areas before they take off will only enhance you profile. 

Business-driven Data 


In the world of Risk Analytics, it is easy to think that if you have the right programming and analytical skills in the right tools, you’ve all got all you need. You might be off to a really good start, but there’s more to it than that. It about having the balance. 

Yes, being data-driven and understanding complex model development is crucial to becoming a good performer in this industry but, what truly separates the good from the great, is business acumen. The ability to understand both what your analytics and models do, and how they impact the overall business is now at the top of most Hiring Manager’s lists. 

A person with good quantitative skills will always see something that can be improved, but they also need to know when to stop and be happy with the result. The key to getting this right lies in their understanding of the business and the ability to answer questions like “If I sit and work on this for 8 more hours, will the real-world difference be worth that amount of time and resource?”. By viewing things through the prism of cost vs reward, and understanding that balance, you can demonstrate that your value to a business goes beyond your analytical skills. 

React, adapt and attract


In this world there are a few things we can take for a certainty; the sky is blue, it will rain on your day off, and there will always be new regulations for financial institutions. Because of the certainty of change, a key thing employers look for in candidates is the ability to react quickly and make changes as soon as they are needed. Fast growing companies such as Klarna, tink and iZettle may seem like fairy-tale success stories, but the real edge they have is their adaptability and agile culture. Whereas some traditional corporations and banks have lengthy and complicated processes required before they adapt to new regulations, these new companies embrace their agility and get things done. 

The ability to be agile and adaptable is, therefore, something that a lot of businesses are starting to realise is key. Therefore, if you’re looking to get ahead, you should try to evolve these qualities in your working ways. If you are looking for something new, look to prove you are driven and do not fear change. If you can demonstrate that you are able to work with a business-oriented mindset and embrace change, you’ll stand out as a key player in your team. 

Specialist vs Generalist 


With the world of Risk Management offering a number of opportunities to become very specialised in very niche areas, it’s worth considering whether this approach is right for you. There are some definite pros, for example, if you are the best developer of PD models for non-retail, you will be highly sought after for roles in this area. Plus, high demand, and a shortage of skillsets means that you will be in a good position to seek a high salary and lots of benefits. However, this does mean that you are likely to only have the opportunity to work in this area for the foreseeable future and, for some, this can become repetitive and not provide enough of a challenge. Additionally, if you were ever were to apply to work in a new area because of this, you would likely find yourself overpriced and needing to take a step down in seniority. 

The alternative, therefore, is to become more of a generalist, with a broader, but less advanced skillset. Think being able to play every instrument, but only knowing one song. There are definitely some clear benefits with this approach, not least the ability to work on a diverse set of projects, gain an excellent understanding of how Risk Management affects a business on every level, and be able to slot into a number of roles easily. You will also gain a better idea of which areas of Risk that you like, and which parts you dislike. Whilst many analysts begin as generalists before looking to specialise when they get promoted, they often find that their knowledge will not be as deep as their specialist counterparts. Therefore, it is likely they will have to take a step-down or make a sideways move before they can achieve that promotion. 

There is no right or wrong when it comes to the specialist vs generalist argument. However, for those looking for faster progression early-on, a generalist approach may be better suited despite the fact that you may need to change approach before reaching the most senior levels. 

Whilst demand will always be high for the best candidates, competition for promotions and senior roles in Risk Analytics remains fierce. Therefore, by proactively thinking about the ways that you work, how effective you are, your business focus, and what your ambitions are, you should be able to get the most out of your career.  

If you’re looking to get ahead in Risk Analytics, we may have a role for you. Take a look at our latest opportunities, or get in touch to see if we can help you take that next step. 

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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.  

Conor Larkin

Conor Larkin

18. March 2021

Why Businesses Need To Put Fraud Prevention Front And Centre

If Fraudsters are anything, they are opportunists. Once the first new stories about COVID-19 started running, it wasn’t long until they were joined by tales of fraudsters selling face masks and hand sanitiser, asking panicked customers to transfer money and then disappearing without a trace.  And it’s not the first time we’ve seen this. Fraudsters are notoriously wise to periods of heightened sensitivity and uncertainty, often preying on the vulnerable. The 2008 financial crisis saw an increase in email-based phishing scams and a decade’s worth of technological advancements means that Fraud remains a many-headed beast.  Add into the mix a change in working styles and environments, and many businesses are more exposed to potential security breaches than they have been in years. Now, more than ever, companies need to make sure their Data is well protected and secure. THE FIRST LINE OF DEFENCE If you’re part of, or leading, a Fraud Prevention team, there are a number of ways you can support your business and keep on top of the situation. Here are just a few: Increase and update your investigation capacity. This team are the front line of your business’ Fraud defence team, interacting with customers daily and spotting new scams. During an uncertain period, retention and team stability is key. These are the people that understand the day-to-day Fraud challenges you face and will be essential in fighting any future challenges.  Sharing Fraud Prevention knowledge is key. Throughout this crisis, trends will be evolving quickly and working collaboratively across teams, and even other businesses, is the best way to combat this. We consistently hear from Fraud Managers that the key to beating Fraud is to share information and knowledge. Despite this, there is always a hesitation amongst companies to admit that they have been a victim to an attack. Perhaps now is the time to change this. Invest in Machine Learning and real time updates for your Fraud defences. Fraud technology has moved on from script writing in SQL and rule changes. Businesses need a real time reactive response and now is an important time to be embracing new technologies. There are a number AI-driven off the shelf packages available or, for a more bespoke solution, a Fraud Data Scientist can create something internally. Educate your team. It may seem simple, but the Fraud team can play a crucial role in minimising any potential risk from human-error. Educating employees on the risks they may face when working remotely, or what scams they need to look out for, is one of the most effective ways of fighting Fraud.  PREPARING YOUR BUSINESS Success in the fight against Fraud isn’t purely down to the group of individuals that make up the Fraud team. As a business, now is the time to be making decisions that can help you stay ahead of the Fraudsters. Here are some considerations: Consider investing in tech as an your immediate response. Not just to bolster your Fraud defences (although there are plenty of vendors offering AI-based solutions), but also technology for your employees to keep work as normal as possible such a sharing platforms, DevOps technology and video calling networks. One of the best ways to block some of the vulnerability loopholes fraudsters are trying to exploit is to keep working habits as close to normal as possible as you move to a remote solution. Be transparent with your customers. Consumers are being incredibly savvy and noting how businesses respond to the pandemic in a way that could have a big impact when normality returns. But they’re also being more empathetic and are willing to understand difficulties. For example, shopping delivery service Ocado were open and transparent when their system could not initially deal with demand. Having communicated the difficulties, worked through their issues and gone the extra mile to let customers know how they can be supported in this time, the received minimal backlash. There is an understanding that we’re all in this together. Finally, if you have the budget, continue to staff up - particularly in competitive fields such as Data Science. A lot of top Data professionals are currently at home and much more accessible than they have been in a long time. With a number of ways to remotely interview and onboard both permanent and contract staff, if you are able to get begin conversations with them now, you’ll have an edge in what will be a very competitive market come later in the year.  If you’re looking to take your next step in the world of Fraud, we may have a role for you, including a number of remote opportunities.  Or, if you’re looking to expand and build out your Fraud team, get in touch with one of expert consultants who will be able to advise on the best remote and long-term processes. 

Rosalind Madge

Rosalind Madge

01. April 2020

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