Risk Analytics

View all team members and how to get in touch below

Director - UK

Recruitment Consultant - UK

Senior Manager - UK

Recruitment Consultant

Recruitment Consultant - UK

Principal Recruitment Consultant - UK

Senior Recruitment Consultant - UK

Latest jobs

Salary

£45000 - £55000 per annum

Location

London

Description

Chance to join an established CRO team at a Digital Marketing Tech business, specialised in delivering large-scale testing projects for a range of customers

Reference:

108736/AO

Expires on
Salary

£45000 - £55000 per annum

Location

London

Description

Established financial institution is looking for a Modelling Analyst to work in their London based office

Reference:

105323/DA

Expires on
Salary

£55000 - £63000 per annum

Location

London

Description

A great opportunity to join a growing team at this global television and film company.

Reference:

111302/LH

Expires on
Salary

€50000 - €55000 per annum

Location

Paris, Île-de-France

Description

Nouvelle opportunité pour un(e) Traffic Manager Senior pour rejoindre une start-up basée dans l'ouest parisien !

Reference:

54847/WC

Expires on
Salary

£45000 - £65000 per annum

Location

London

Description

As the digital analytics SME, you'll work with data scientists to stitch together disparate data sets to build out and develop more rounded customer views

Reference:

66598

Expires on

Harnham blog & news

With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.

Visit our Blogs & News portal or check out our recent posts below.

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.  

The Reliability Of Sleep Trackers For Sleep Data

One in three of us regularly suffer from poor sleep. By this we mean not entering the correct stages of the sleep cycle often enough. During the optimum eight hours of slumber, we should be getting per night, the body should enter three different stages of sleep on a cyclical rotation: light, deep and rapid eye movement (REM). The most important stage of this being deep sleep, of which a healthy adult should be entering for around one to two hours.  Unfortunately, it is often the case, for a vast number of reasons, that many adults struggle to wake up feeling refreshed. From absorbing too much blue light from screens before bed, poor dietary habits or increased levels of stress, there are many factors into why good sleep eludes nearly a third of us daily. Over the past year especially, as a direct result of the pandemic, our sleepless nights have become increasingly worse. It seems anxiety related to COVID-19 has spiked our inability to get good rest. What are the dangers of persistent low-quality sleep? Continual restless nights can have profound effects on both our bodies and our minds. It can place immense stress on the immune system, increasing the risk of becoming seriously ill. Other life-threatening diseases also linked with poor sleep include obesity, heart disease and diabetes.  Our mental state can also be incredibly damaged by consistent poor sleep. Not only does our ability to concentrate reduce, but our susceptibility to mental ill-health, such as depression, increases too.  It is no surprise then that, as a global population, our obsession with the amount of sleep we get per night has skyrocketed in the past few years, consequently seeing the boom of sleep tracking technology. From wearable tech such as the Fitbit and Apple Watches, to other bedside devices and bed sensors, the market for sleep trackers is estimated to reach $62bn in 2021 alone. But is this technology a reliable source of data for our sleep patterns? The problems with sleep trackers Wearable technology can only go so far when it comes to measuring our quality of sleep. Watches especially can usually measure aspects of our body such as heart rate and movement – all of which can be used as indicators of restfulness. However, their consistent accuracy is questionable. According to research, sleep trackers are 78 per cent accurate when it comes to identifying whether we are awake or asleep, which is a pretty good statistic for developing technology, however, this drops dramatically to 38 per cent when estimating how long it takes for users to fall asleep. For true accuracy, sleep should be measured through brainwave activity, eye movement, muscle tension, movement and breathing – all of which can only be looked at through a medical polysomnogram.  Additionally, much like many other sources of technology, sleep trackers have become a troublesome culprit for obsessive behaviour. In 2017, scientists coined the term Orthosomnia, the recognition of a real problem many were, and still are, having with become obsessive, to the point of mental ill-health, around tracking sleep. As stated by neurologist, Guy Leschziner; “If you have a device that is telling you, rightly or wrongly, that your sleep is really bad then that is going to increase your anxiety and may well drive more chronic insomnia." However, sleep trackers aren’t all bad. While not a tool to be used for sleep disorder diagnosis, they can be useful gadgets to help rethink our sleep habits to aim for a better night’s sleep.  The positives of sleep trackers While questions around the accuracy of this technology are prominent, trackers, overall, are pretty good when it comes to recording total sleep time. If used as a guide rather than an aid, sleep trackers can help users get into better sleep habits which in turn will undoubtedly improve their quality of sleep.  If the data is showing that users are only achieving five hours of sleep per night, and they are going to bed very late and rising early, then users may be encouraged to practice better sleep hygiene. From removing any blue light from the bedroom space, to taking an hour before bed to engage in less stimulating activities, such as reading, and practicing methods such as mindfulness or meditation to induce relaxation.  Sleep data from trackers can also be a useful tool to begin conversations with health professionals. Someone who regularly finds themselves groggy in the morning, with the notion that their sleep is badly disturbed, may find solace in sleep tracking data and it may give them the confidence to seek relevant help. While this sort of technology and its data will not be the end point for a diagnosis, it may give both the user and their doctor insight into any potential problems or issues they may be having with sleep.  Ultimately, those using sleep trackers shouldn’t be losing sleep over the data they present. Instead, ensure you are taking the analysis provided with a pinch of salt, and explore this in tandem with how you feel in yourself to assess whether you need to make changes to your sleep routine or seek help for a potential sleep disorder. Data is an incredibly important too, but using this in the right way is absolutely critical. If you're looking for a new role to get you out of bed in the morning or to build up your dream data team, 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.