Credit Pricing Analyst

London
£40000 - £50000 per annum + Comprehensive Benefits Package

Pricing Analyst
Up to £50,000 plus competitive benefits package
London

A fast-paced Credit Card provider are looking a Credit Risk, Pricing or Strategy Analyst to come into the team and help them assess customer credit behaviour and drive profitability for new customers. You'll be using strategic analysis and financial modelling (NV) to drive profitable business decisions within their credit risk framework

WHO WILL YOU BE WORKING FOR?

One of the UK's largest and fastest growing Credit Card company, who are investing more money than ever into the company's analytics functions.

WHAT WILL YOU BE DOING?

  • Developing and optimising the company's Credit Risk and Pricing strategies through data, analysis and modelling
  • Ownership of strategic projects in an end to end fashion, including championing your ideas through to implementation
  • Enhancing the company's decision making framework through the use of Data and CVM/NPV Models (financial modelling)
  • Become a subject matter expert and show leadership across both the new and existing customer management functions.

WHAT IS REQUIRED FROM YOU?

  • Experience developing Credit Risk or Pricing strategies within the lending space is required
  • Unsecured lending experience is an advantage
  • Experience in the development of CVM or NPV Models is ideal
  • Proven Stakeholder Management experience
  • Strong coding experience (Either Python, SAS or SQL)
  • Ability to prioritise and handle large project workloads

BENEFITS

You should expect to earn up to £50,000 plus a competitive benefits package (10% bonus, 12% pension, private medical, 26 days of AL + Bank Holidays)

HOW TO APPLY?

Please register your interest by sending your CV via the Apply link on this page.

KEYWORDS

Credit Risk - Python - SAS - SQL - NPV - CVM - Modelling - Customer Analytics - Strategy - Portfolio - Risk - Investment - Customer Management - Acquisitions - Collections - Credit Strategy - Pricing

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Harnham blog & news

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

Using Data Visualisation To Bring Data & Analytics To Life

The majority of the human population are visual learners. Our brains are wired in such a way where we can register 36,000 visual messages per hour, and visuals are processed 60,000 times faster than text. In short, one of the best ways to truly assimilate and understand new-found knowledge is through clear and digestible imagery.  Because of this valuable insight, we are now witnessing the fast-growing trend of Data Visualisation. Over the next six years, the value of Data Visualisation tools is expected to reach $19.2 billion, over double what it was in 2019.  Data & Analytics is one key area where data visualisation is used continuously. The raw data collected on a daily basis by Data Analysts can be incredibly time-consuming to sift through, not forgetting near-impossible to form palatable findings from. However, through the use of data visualisation tools such as graphs, heat maps, charts and infographics, confusing, text-based data can be transformed and brought to life. So, how can Data Visualisation help your business? Greater understanding of your data As Lydia, our Senior Recruitment Consultant, stated in her most recent article – data insights have the capability of not only improving decision-making, but also allow you to spot key trends, errors and predict future challenges. Nevertheless, all of these brilliant capabilities of data insights can only occur when teams can garner an in-depth understanding of the data being presented to them.  Without a background in statistics, which very few members of any team would possess, the raw data simply wouldn’t mean anything, and key insights could be missed. Utilising data visualisations not only makes data more tangible, but it also allows every team member to understand the data, make decisions and implement changes more efficiently. Standing out from the competition The effectiveness of Data Visualisation is no secret, and time and time again it’s been proved that this way of presenting data is far more likely to produce results than simply reviewing text.  Research within Analytics Insight reported that businesses using data discovery tools are 28 per cent more likely to find timely information compared to their dashboard-using counterparts, and 48 per cent of business intelligence users at companies with visualisation tools are able to find the information they need without the help of a specialist team.  Nevertheless, despite the incredible benefits, only 26 per cent of businesses globally are using data visualisation tools.  While the reasons for this slow uptake are varied, it’s clear that those companies who are willing to invest in Data Visualisation are far more likely to stand a head above their competitors. It can improve customer experience 98 per cent of companies will use data to help drive a better customer experience, but it doesn’t always mean that this data is collected, managed or presented well.  Data is, and should be, used as a way to back up what brands are saying, especially if they’re shouting from the rooftops about how fantastic they are.  When a business or brand uses accurate Data Visualisation to tell this story – for example, the percentage of consumers who report high levels of customer satisfaction, or the amount of money donated to CSR projects – audiences will respond much better than if the claim appears to be empty words without any evidence.  Data Visualisation is undoubtedly one of the most effective ways to communicate data, both internally and externally. The comprehensible formats available enables information to be processed with ease, and for learnings and understandings to be absorbed and implemented with much more efficiency than text-based raw data. It’s clear that this trend is only going to grow in popularity as businesses begin to put more investment behind it in order to reap the benefits and watch the positive impact on their bottom lines prosper.  For examples of how Harnham uses Data Visualisation, head over to our recent research reports.  If you're looking to take the next step in your career or build out your Data & Analytics team, we can help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more. 

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.  

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