Senior Fraud Analytics Manager
Manchester, Greater Manchester / £50000 - £67000
£50000 - £67000
Manchester, Greater Manchester
SENIOR FRAUD ANALYTICS MANAGER
UP TO £67,000
Join a leading retailer in Manchester to help lead their fraud analytics team!
As a Fraud Analytics Senior Manager, you will be:
- Support the development of a cutting-edge fraud protection system at a leading retailer
- Using fraud trends to generate value for the business and customers
- Building, implementing, and testing fraud models and amending fraud rules using SQL and python
- 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 authorization strategies using large data sets
- Managing and mentoring junior analysts
- Have a very strong financial services analytics background
- Strong skills in SAS, SQL, or Python
- Excellent communication skills
- A numeric degree
- A background in fraud analytics
- Up to £67,000 + competitive benefits package
- Hybrid working
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.
Key Fraud Trends: How to Stay Safe in the Changing Fraudscape | Harnham Recruitment post
Sharing and collecting data is part of our everyday lives. Whether our information is shared over social media, e-commerce sites, banks, or elsewhere, this can open up risks. 2017 saw the highest number of identity fraud cases ever, an increase in young people ‘money muling’ and higher bank account takeovers for over-60s. Whilst overall fraud incidences fell 6%, these cases highlight just some of the changing trends as fraud issues stem more from misuse than ever before.Dixons Carphone, Facebook and Ticketmaster are just three cases you may recognise from a string of high profile data breaches this year. Technological advances, more accessible and available data, coupled with an increased sophistication of fraud schemes, makes it more likely that data breaches and fraud attacks will become regular news items. But how is the fraud landscape changing and can technological advances be advantageous in detecting and reducing fraud?Identity fraud increasing for under 21sIn June 2018, Dixons Carphone found an attack enabled unauthorised access to personal data from 1.2 million customers. It’s now been uncovered that the number is much higher, closer to ten times initial estimates. Whilst no financial information was directly accessed, personal data such as names, addresses and emails enable fraudsters to fake an identity. Younger fake identities are used more for product and asset purchases which typically require less stringent checks, such as mobile phone contracts and short-term loans. In 2017, Cifas, a non-profit organisation working to reduce and prevent fraud and financial crime, reported the highest number of identity fraud cases ever. Under 21s are most at risk seeing a 30% increase as they engage more with online retail accounts. Whereas previously identity theft would manifest itself in fraudulent card and bank account activity, it’s now being used to make false insurance claims and asset conversion calling for stronger detection in these industries. Young People Used as Money MulesThis age group aren’t only being targeted for identity theft; there’s a 27% uplift in young people acting as money mules. ‘Money muling’ is a serious offence that carries a 14-year prison sentence in the UK. In most cases, younger people are recruited with the lure of large cash payments to facilitate movement of funds through their account, taking a cut as they go. In a world where young lives are glamourised and luxurious goods are displayed over social media, this cut can be particularly appealing. Whether aware, believing the reward outweighs the risk, or unaware a money laundering crime is being committed, deeper fraud controls are needed across social media as much as bank accounts. This raises the question as to whether banks should be linking social media to customer details to stop money laundering early on?Increased bank account takeover for over 60sCifas also reported an increase in account takeovers for over 60s for the same period. Seen by fraudsters as a less tech-savvy and therefore more susceptible demographic, over 60s are increasingly being targeted with online and social engineering scams. The same features which can make some over 60s a target for these scams, can also mean that account takeovers are not immediately noticed and reported, posing yet another difficulty for fraud monitoring and prevention. Vigilance and proactiveness is key. Here are three tips to get you started:Never give personal or security information to someone who contacts you out of the blue, either online, on the phone, or face to face. Always phone and check with the company first. If you make the call then you know you can trust the person on the other end.Check with your bank to see if they offer an elder fraud initiative such as a monitoring service that scans for suspicious activity and alerts customers and their families or educates seniors on types of scams and how to avoid them.When in doubt about something, delay and seek a second opinion.Check with your local library, government offices, or non-profit organisation for more top tips to stay safe from scams and social engineering. Industry approachTraditionally, financial services organisations have been at the forefront of developing fraud controls; they are often the ones most impacted by the financial risk (the monetary cost of the attacks on their business) and regulatory risk (ensuring their business is adhering to regulations and controls).However, with modern day trends and the changing nature of fraud, all industries need to be focused on reputational risks and prevention. Single big events like Facebook and Dixon Carphone’s data breaches can have a far-reaching impact. But, there is light at the end of the tunnel. Monzo, an online bank, which bills itself as the future of banking has stepped up the game when it comes to their customer’s security. Upon reports of fraudulent activity on customer cards, they took immediate action to correct the problem. Then they took things a step further, introducing digital analytics to help identify trends and patterns. As patterns emerged, Monzo then notified both the breached business and the authorities.Perhaps a cross-industry collaborative approach is needed as, after all, fraudsters are collaborating. By doing so, businesses will become more proactive, rather than reactive, and can put measures in place to stop potential fraud.If you’ve got a nose for numbers and want to help secure the reputation of businesses the world over, we may have a role for you. To learn more, call our UK team at +44 020 8408 6070 or email us at firstname.lastname@example.org
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