Fraud Analytics Manager
Leeds, West Yorkshire / £60000 - £70000
£60000 - £70000
Leeds, West Yorkshire
Fraud Analytics Manager
Up to £70,000
An excellent opportunity for an experienced fraud professional to work for an international Bank. This bank provides an excellent banking service across the world and need someone with Fraud Analytics experience to help protect its customers.
As a Fraud Analytics Manager, you will be:
- Support the development of cutting-edge fraud detection systems.
- Using fraud trends to generate value for the business and customers.
- Building, implementing, and testing fraud models using both third-party data suppliers and transaction monitoring
- Giving feedback to the teams to help drive improvements in the systems and models to improve efficiency
- Managing fraud and financial crime risk across many products
- Driving fraud insights and strategies across payment fraud
Your skills and experience
For you to be successful as a Fraud Analytics Manager, you will need:
- Have a very strong fraud analytics background
- Experience working with payments fraud (or transaction fraud)
- Strong skills in SAS, SQL, or Python
- Excellent communication skills
- A numeric degree
Up to £70,000 + competitive benefits package
HOW TO APPLY
Please register your interest by sending your CV to Sean Tunley via
As Incidents Of Cybercrime Increase, How Can A Fraud Analyst Give Your Business Peace Of Mind?
Whilst it’s true that cybercriminals are becoming more creative and sophisticated, as are analytical techniques and the experts that wield them. Fraud Analysts now have more techniques and reach than ever, and as incidents of cybercrime increase, this isn’t an area that businesses should be scrimping on.
According to PwC’s Global Economic Crime and Fraud Survey 2022, 46 per cent of organisations surveyed reported experiencing fraud or financial crime over the last 24 months and tech, media and telecommunications businesses appeared to have taken the brunt. Findings showed that nearly two-thirds of this group experienced some form of fraud, the highest incidence of any industry.
The ONS also recently released stats showing that fraud offences increased by 25 per cent in 2021 (to 4.5 million offences) compared with the year ending March 2020. Indeed, the proportion of these incidents that were cyber-related increased to 61 per cent up from 53 per cent.
The rise of cyber-fraud is a clear issue and for some businesses such as financial institutions, tackling this by using fraud teams made up of expert Fraud Analysts is the norm. But for others, it may not have been seen as a priority until recently. However, any business which has a growing number of online transactions will become a bigger target for fraudsters and would benefit from a team member able to help minimise the risk.
So, how can fraud analysts help?
Far from wanting to paint a bleak picture, while fraud techniques are evolving and improving, so are anti-fraud efforts. All risks associated with financial crime involve three kinds of countermeasures: identifying and authenticating the customer, monitoring and detecting transaction and behavioural anomalies, and responding to mitigate risks and issues. All of these are carried out by fraud experts, such as Fraud Analysts, armed with ever-evolving technologies and techniques. So, what exactly does a Fraud Analyst do?
Fraud Analysts will track and monitor transactions and activity, identify and trace any suspicious or high-risk transactions, determine if there is improper activity involved, and identify if there is any risk to the organisation or its customers. They are able to digest huge swathes of information and quickly and efficiently prioritise the data that’s important in order to tell a story of fraud or no fraud.
To cope with the speed and scale of online commerce, new technologies such as Machine learning (ML) models have come to the fore. These models have the ability to simulate thousands of scenarios and take over the mundane tasks of sifting through swathes of data in a tiny percentage of the time it would take a human. The systems used by Fraud Analysts will vary based on the industry, but a common example is rule-based expert systems (RBESSs). A very simple implementation of artificial intelligence (AI) RBESSs are used to detect fraud by calculating a risk score based on users’ behaviours, such as repeated log-in attempts or ‘too-quick-for-being-human’ operations. Based on the risk score, the rules deliver a final decision on each analysed transaction, therefore blocking it, accepting it, or putting it on hold for analyst’s revision. The rules can be easily updated over time, or new rules can be inserted following specific needs to address new threats.
This method has proved very effective in mitigating fraud risks and discovering well-known fraud patterns. That said, rule-based fraud detection solutions have demonstrated that they can’t always keep pace with the increasingly sophisticated techniques adopted by fraudsters, without regular updates and expert use.
Machines also cannot mimic human traits like intuition. People can detect if things aren’t right even if they have not seen them before. It’s an instinct not yet successfully trained into machines. Therefore, new trends are much better pursued by an analyst and then a machine can be trained to stop future occurrences. A well-implemented ML system will free up precious time for an analyst to perform these more productive tasks.
A non-stop process
So, your Fraud Analyst has now set up a new ML system to identify fraudulent activity and is also looking for new trends that fraudsters may be trying – now what? Fraud Analysts never sit still. Their job is not a one-time fix but one of constant evolution and refinement. Their role involves identifying weaknesses in systems and continually looking for opportunities for improvement, such as recommending anti-fraud processes to detect new patterns or new software tools to help with reporting. Their finger is always on the pulse of emerging developments and will ensure your company remains protected against current risks.
Not only is this aspect part of the job description, but it is also to some extent inherent to their nature. Fraud Analysts tend to be curious, have a strong attention to granular detail, as well as an inclination towards problem-solving. Leaving no stone unturned is part of their makeup. This analytical skillset will dig out any problems that are there – which will unfortunately then require you to fix them (sorry!) – but it is far better to be aware of any weaknesses now. The majority of companies only realise their shortcomings when it is already too late. Ultimately it is better to be safe than sorry.
A Fraud Analyst not only helps to protect businesses against creative cyber criminals but will also give owners reassurance as they look to grow and thrive unimpeded.
If you are looking for a complete recruitment solution across the breadth of Data & Analytics disciplines to build out a robust Data & Analytics function, get in touch with one of our expert consultants here.
Looking for a new role? Take a look at our latest Fraud Analyst jobs.
How Fraud Analytics Can Keep Your Money Safe
How Fraud Analytics Can Keep Your Money Safe
We’ve previously written about how data analytics can help save your business money, but what about protecting the funds and resources your company already has?
It is widely reported that cyberattacks are rising as are incidences of fraud. Indeed, a 2020 PwC study found that 47 per cent of businesses had at least one incidence of fraud in the past two years, with an average of six instances per company. The losses from these incidents for the 5,000+ businesses surveyed amounted to $42 billion – approximately $8.4 million per company.
As consumer costs rise and businesses find their budgets stretched more than ever, losing any funds through fraud has become all the more damaging. Because of this, leaders need to pull out every stop to prevent it. Here’s how fraud analytics can help.
What is Fraud Analytics?
Companies have been using anomaly detection and rules-based methods to combat fraud for decades. While these methods are effective, they have their limitations.
This is because rules-based tools only detect abnormalities based on explicit, pre-written rules, whereas advanced analytics uses a company’s existing data to spot patterns, learn trends, and eventually detect outliers on its own through the use of artificial intelligence, machine learning, and predictive analytics.
These advanced analytics tools can be used to automate and speed up some of the labour-intensive work, which reduces operational costs and leaves others free to concentrate on the arguably more powerful, preventative activity.
One sector that’s been heavily leveraging fraud analytics is finance. Traditionally, such organisations have relied heavily on manual, human intervention in the regulatory reporting process. However, with large swathes of data moving in and out of systems, the capabilities for humans to keep up are simply untenable.
How is Fraud Analytics Useful?
Financial data can be scrutinised in numerous ways to identify anomalies in patterns of consumer and/or employee behaviour that might indicate financial wrongdoing–both internal and external.
- Ledger entries can be scrutinised for potential fraud or errors, using data analytics to identify suspicious entries.
- Expenses in areas such as travel are often where unscrupulous employees could fudge numbers. This could be tackled by monitoring department spending over time to understand the average range for each division, and setting up an alert triggered if the department deviates from that range.
- Contractor payments are common areas for fraudulent behaviour. Vendors may submit the same invoice multiple times, either by accident or to follow up on unpaid bills. You may pay the same invoice twice if you don’t have a system for tracking and flagging duplicates.
Financial data analytics can also be applied to a range of companywide performance indicators, such as monitoring company goals and objectives, building dynamic profit and loss statements, or streamlining budgeting and forecasting.
By evaluating historical data alongside forward-looking financial statements, analytic techniques can help to form an evolving forecast, which gives finance teams a greater understanding of the current and future financial health of the business. And, unlike the static reports used for accounting, data analytics offers dynamic analysis, allowing the user to ‘ask’ the data questions.
Humans Versus Machines
Despite strides in technological development, human intervention remains paramount in data analytics practices. While analytics techniques offer a fool-proof way of identifying issues, humans are needed to provide vital context, investigate suspicious activity and give it business relevance.
There will always be a high number of anomalies from the data analytics process, but very few will transpire to be errors and even fewer fraudulent transactions. Data professionals with an understanding of the business can use their judgment and intuition to weed out irrelevant information, explain most anomalies that appear, and further investigate those that warrant extra attention.
Interested in using your skills to help businesses to remain secure against fraud? The world of fraud data analytics is a fast-paced industry full of opportunities across countless sectors – check out our roles today.
Weekly News Digest: 10th – 14th January 2022 | Harnham Recruitment post
This is Harnham’s weekly news digest, the place to come for a quick breakdown of the week’s top news stories from the world of Data & Analytics.PYMNTS.com: Fighting fraudulent transactions, by the numbersHow are banks using AI and other tools to curb transaction fraud?In 2021, PYMNTS interviewed banking executives to determine how acquiring banks use artificial intelligence (AI) and effective merchant monitoring to combat credit, debit, and prepaid card fraud. In this piece, it shares the results of the interviews:Most acquiring banks say fraudulent transactions increased between 2020 and 202193 percent of those surveyed said they saw a year-to-year increase in fraud. 88 per cent said reducing fraud is critical to their ability to increase or maintain merchant processing revenue.Most banks that use AI use it for fraud detectionAlmost all (98 per cent) of acquirers using AI said it has found fraud detection. 60 per cent have said AI is the best tool for them to detect fraud, while another 15 per cent said it’s an important weapon.Most banks outsource this workFraud detection is too important for some banks to spend years developing their own complex system. So, 92 per cent of banks that use AI systems for fraud prevention and detection said they outsource the systems.To read more about this, click here. Analytics Insight: Top Python machine learning libraries to explore in 2022What Machine Learning libraries should you be focusing on this year? Python is the most popular programming language for data science projects, while machine learning is globally trending. According to Analytics Insight, Python machine learning libraries have become the language for implementing machine learning algorithms. So, to fully understand Data Science and Machine Learning, Python is essential. Here are the top Python machine learning libraries to help you begin your Python journey, and what they’re most useful for:TensorFlow: an open-source numerical computing library for machine learning based on neural networks.PyTorch: used for natural language processing, computer vision, and other similar kinds of tasks.Keras: machine learning toolset that aids companies such as Square, Yelp and Uber.Orage3: includes tools for machine learning, data mining, and data visualisation. Numpy: includes robust computing capabilities within the large, high performance programming communitySciPy: a core tool for accomplishing mathematical, scientific and engineering computations.SciKit-Learn: an indispensable part of the technology stacks of Booking.com, Spotify, OkCupid, and others.Pandas: has powerful data frames and flexible data handling.Matplotlib: replaces the need to use the proprietary MATLAB statistical language. Theano: allows for simultaneous computing, fast execution speed and optimised stability. To read more about this, click here. Analytics India Mag: Why should data engineers learn Scala?Is Scala beneficial to a Data Engineer? Scala combines object-oriented and functional programming in one concise, high-level language, and its static types help avoid bugs in complex applications. Scala does have some key advantages such as its use of data-parallel operations, simple structure suitable for big data processors, and its high-volume capabilities. On the other hand, the article points out why Scala might not be beneficial to a Data Engineer:Difficult to learn Not widely adoptedOnly 10 per cent of jobs require Scala knowledge While Scala does not occupy the same level of importance as other popular languages, it’s certainly a useful language to learn if it matches a data engineer’s career goals. To read more about this, click here. Forbes: Data analytics marathon – why your organisation must focus on the finishIn this Forbes piece, the author compares analytics to a marathon – both take commitment preparedness, and endurance to be successful. A companies’ analytics will go through several cycles as business priorities shift and evolve. They are explained here as milestones of the Data & Analytics marathon:Data collectionData preparationData visualisation Data analysis Insight communicationTake action The author, Brent Dykes, notes that many drop off at the last mile in the race, the action phase where analytics teams perform analysis, share their insights and then implement changes to optimise the business. Most companies have no problem with the start of the data analytics marathon, but many of them aren’t completing the entire race. In order to finish the data analytics race in a strong position, companies and analytics teams must align the data with the business strategy and follow these three steps.Automate early-stage tasksNarrow the scopeFoster a stronger data cultureTo read more about this, click here. We’ve loved seeing all the news from Data & Analytics in the past week, it’s a market full of exciting and dynamic opportunities. To learn more about our work in this space, get in touch with us at firstname.lastname@example.org.
CAN’T FIND THE RIGHT OPPORTUNITY?
If you can’t see what you’re looking for right now, send us your CV anyway – we’re always getting fresh new roles through the door.