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AI for fraud detection: beyond the hype

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AI for fraud detection: beyond the hype

Sundeep Tengur, Senior Business Solutions Manager at SAS

The financial services industry has witnessed considerable hype around artificial intelligence (AI) in recent months. We’re all seeing a slew of articles in the media, at conference keynote presentations and think-tanks tasked with leading the revolution. AI indeed appears to be the new gold rush for large organisations and FinTech companies alike. However, with little common understanding of what AI really entails, there is growing fear of missing the boat on a technology hailed as the ‘holy grail of the data age.’ Devising an AI strategy has therefore become a boardroom conundrum for many business leaders.

How did it come to this – especially since less than two decades back, most popular references of artificial intelligence were in sci-fi movies? Will AI revolutionise the world of financial services? And more specifically, what does it bring to the party with regards to fraud detection? Let’s separate fact from fiction and explore what lies beyond the inflated expectations.

Why now?

Many practical ideas involving AI have been developed since the late 90s and early 00s but we’re only now seeing a surge in implementation of AI-driven use-cases. There are two main drivers behind this: new data assets and increased computational power. As the industry embraced big data, the breadth and depth of data within financial institutions has grown exponentially, powered by low-cost and distributed systems such as Hadoop. Computing power is also heavily commoditised, evidenced by modern smartphones now as powerful as many legacy business servers. The time for AI has started, but it will certainly require a journey for organisations to reach operational maturity rather than being a binary switch.

Don’t run before you can walk

The Gartner Hype Cycle for Emerging Technologies infers that there is a disconnect between the reality today and the vision for AI, an observation shared by many industry analysts. The research suggests that machine learning and deep learning could take between two-to-five years to meet market expectations, while artificial general intelligence (commonly referred to as strong AI, i.e. automation that could successfully perform any intellectual task in the same capacity as a human) could take up to 10 years for mainstream adoption.

Other publications predict that the pace could be much faster. The IDC FutureScape report suggests that “cognitive computing, artificial intelligence and machine learning will become the fastest growing segments of software development by the end of 2018; by 2021, 90% of organizations will be incorporating cognitive/AI and machine learning into new enterprise apps.”

AI adoption may still be in its infancy, but new implementations have gained significant momentum and early results show huge promise. For most financial organisations faced with rising fraud losses and the prohibitive costs linked to investigations, AI is increasingly positioned as a key technology to help automate instant fraud decisions, maximise the detection performance as well as streamlining alert volumes in the near future.

Data is the rocket fuel

Whilst AI certainly has the potential to add significant value in the detection of fraud, deploying a successful model is no simple feat. For every successful AI model, there are many more failed attempts than many would care to admit, and the root cause is often data. Data is the fuel for an operational risk engine: Poor input will lead to sub-optimal results, no matter how good the detection algorithms are. This means more noise in the fraud alerts with false positives as well as undetected cases.

On top of generic data concerns, there are additional, often overlooked factors which directly impact the effectiveness of data used for fraud management:

  • Geographical variances in data.
  • Varying risk appetites across products and channels.
  • Accuracy of fraud classification (i.e. which proportion of the alerts marked as fraud are effectively confirmed ones).
  • Relatively rare occurance of fraud compared to the huge bulk of transactions; having a suitable sample to train a model isn’t always guaranteed.

Ensuring that data meets minimum benchmarks is therefore critical, especially with ongoing digitalisation programmes which will subject banks to an avalanche of new data assets. These can certainly help augment fraud detection capabilities but need to be balanced with increased data protection and privacy regulations.

A hybrid ecosystem for fraud detection

Techniques available under the banner of artificial intelligence such as machine learning, deep learning, etc. are powerful assets but all seasoned counter-fraud professionals know the adage: Don’t put all your eggs in one basket.

Relying solely on predictive analytics to guard against fraud would be a naïve decision. In the context of the PSD2 (payment services directive) regulation in EU member states, a new payment channel is being introduced along with new payments actors and services, which will in turn drive new customer behaviour. Without historical data, predictive techniques such as AI will be starved of a valid training sample and therefore be rendered ineffective in the short term. Instead, the new risk factors can be mitigated through business scenarios and anomaly detection using peer group analysis, as part of a hybrid detection approach.

Yet another challenge is the ability to digest the output of some AI models into meaningful outcomes. Techniques such as neural networks or deep learning offer great accuracy and statistical fit but can also be opaque, delivering limited insight for interpretability and tuning. A “computer says no” response with no alternative workflows or complementary investigation tools creates friction in the transactional journey in cases of false positives, and may lead to customer attrition and reputational damage – a costly outcome in a digital era where customers can easily switch banks from the comfort of their homes.

Holistic view

For effective detection and deterrence, fraud strategists must gain a holistic view over their threat landscape. To achieve this, financial organisations should adopt multi-layered defences – but to ensure success, they need to aim for balance in their strategy. Balance between robust counter-fraud measures and positive customer experience. Balance between rigid internal controls and customer-centricity. And balance between curbing fraud losses and meeting revenue targets. Analytics is the fulcrum that can provide this necessary balance.

AI is a huge cog in the fraud operations machinery but one must not lose sight of the bigger picture. Real value lies in translating ‘artificial intelligence’ into ‘actionable intelligence’. In doing so, remember that your organisation does not need an AI strategy; instead let AI help drive your business strategy.

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The case for AI technology adoption in financial back-office roles to improve efficiency

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The case for AI technology adoption in financial back-office roles to improve efficiency 1

By Tomas Gogar, AI CEO, Rossum

In this era, digital transformation isn’t anything new. Nonetheless, it can still cause a lot of confusion and resistance for some companies, many of which are often slow, unwilling or unable to implement the necessary changes to embrace technology. As a result, entire industries are barely scratching the surface when it comes to shifting to the digital world, and many, from the insurance industry to logistics and delivery are still catching up on the digital transformation.

The banking and financial sector have been notoriously slow in adapting to the online world. They paid the high price for it, giving way to a flurry of incredibly successful new disruptive players, built on cutting edge tech from the ground up. From Transferwise, Revolut or Venmo, to GoCardless, this new generation of fintech companies addressed consumers changing expectations in a way that traditional retails banks simply couldn’t.

To catch up, incumbent players have prioritised the user interfaces, giving the appearance of a digital offering, and oftentimes leaving the back end infrastructure untouched, and hence the processing power, accuracy and speed unaffected. Back-office functions, although they are essential to the smooth running of a business, have seen very little change and as a result,  too many people in these functions are still tied up typing information into spreadsheets and software forms – in fact, manual data entry is a prime example of how much resources the offline legacy wastes. Take Accounts Payable for example, invoice data entry in this sector is estimated to eat up roughly 100 human lives worth of time every single day.

Tomas Gogar

Tomas Gogar

With the significant increase in the number of employees working from home due to the global COVID-19 pandemic, the back-office challenges have suddenly come to light, and finally, companies that got away with minimal changes so far, are realising that they need a structural digital overhaul, and fast. We believe the solution to this is artificial intelligence backed software solutions.

Previous technology based solutions essentially did half the job, heavily depending on human fact checking. Consequently, these solutions were actually quite cumbersome and time consuming and costly to implement and maintain, and offered only incremental improvements. Now with AI, automises data processing completely removing the need for human fact checking (and human error!). Additionally, deployment is massively simplified with an average setup time of one week, compared to about 6 months for previous technologies.   AI solutions are also highly adaptable to new formats and scenarios, allowing businesses to test them in say one department and to quickly roll out a single unified solution across all functions of the business.   Data can be extracted from any invoice layout with no template or rule set-up, saving significant and effort. Rather than trying to change and standardise a highly fragmented environment (there are about as many invoice formats as there are businesses), AI can work with it, and optimise the overall process and offer a unified answer to a fragmented ecosystem.

Taking Accounts Payable as an example again, this is a sector that has relied by and large on Optical Character Recognition (OCR) software solutions in an attempt to remove some of the manual labour involved in reading processing and filing invoices. Although OCR did improve the processes to a certain degree, ultimately these types of solutions still required a long and expensive set up processes and a lot of manual labour to actually capture the data accurately with templates and manual data entry. Now, with AI software, like the one we have created, this is a solution that makes data extraction simple and easy, saving time and man power, as well as building on existing infrastructure. It has the ability to transform this industry.

In conclusion, for a sector that has been slow to adopt digital change, AI is THE technology answer that is finally fixing the invisible pain points that businesses had simply accepted as unremovable. AI applied in this way offers a viable way forward and businesses that were notoriously slow and resistant to embrace the digital transition, incentivised to make a change, may actually end up at the head of the pack. Skipping ‘older tech’ and jumping straight into AI solutions, the best scenario available by far, is indeed the smartest, fastest and most cost effective way to transition into the digital world.

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InsurTech is helping to drive the digital evolution of the UK motor retail industry

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InsurTech is helping to drive the digital evolution of the UK motor retail industry 2

By Alan Inskip, Tempcover CEO & Founder

If the last nine months have made anything clear, it is that the pandemic has fundamentally changed both buying and driving habits for UK motorists. The latest Tempcover research has revealed that online-only used car sales had increased fifteen-fold during the pandemic among 2,000 survey respondents.

Before lockdown, just 4% of used car sales were fully-digital. The vast majority of those surveyed opted for either a physical purchase (50%) or a digitally-assisted purchase (45%), relying on a combination of digital tools and an in person viewing or road test before buying.

While car sales overall are down on last year’s figures*, one in six (17%) of those surveyed had bought a used car during lockdown, with two thirds (64%) relying on a fully-digital purchase journey. Digitally-assisted purchases counted for one in five (20%) used car sales, while in person sales fell to just 15% – no surprise considering the ongoing social distancing measures.

And when it comes to arranging insurance for their recently-purchased vehicle, our survey participants displayed an equal balance between telephone and online as the preferred method (48% each). Nearly a third of those (28%) said they wait up to ten minutes for their policy to be confirmed, and a further 22% wait as long as 20 minutes to get cover.

The switch to digital insurance, driven by InsurTech

In the midst of rapid and significant market changes, many traditional insurers have lacked the agility and flexibility to adapt accordingly. InsurTech can provide immense value in bridging that gap, as the digital solutions are entirely scalable, with the flexibility to substantially increase in size and across multiple geographies, with minimal disruption.

Alan Inskip

Alan Inskip

The ongoing decline of physical transactions in the motor retail industry is a perfect example of how InsurTech is adding value. Several national blue-chip dealerships, with both physical and digital showroom floors, are already streamlining their online purchase process by offering temporary driveaway insurance policies to cover the vehicle for a fixed-term, usually between five to seven days, as part of the purchase journey.

The entirely online one-step user experience is the first of its kind in the traditionally outdated and inflexible driveaway insurance industry and it is dramatically simplifying the process of how insurance is purchased and consumed. Due to the flexibility and agility of the digital solution, each retailer has its own unique URL, where the customer can obtain a simple single-cost policy in just 90 seconds through an entirely digital process, which fits in line with the evolving consumer purchase trends.

For the dealers, this technology means more efficient stock clearance times and greater profitability. For the buyers, it takes the stress out of searching for annual insurance on the spot, and provides the driver with near instant cover so that they can immediately drive their new car, while giving them the opportunity to thoroughly research the best annual policy to suit their needs. An added benefit is there’s no risk to any existing No Claims Discount, as it’s a separate and standalone policy.

While there is a chance these trends will reverse to some extent post pandemic, it is clear that the consumer appetite for digital purchase and consumption is here to stay, and InsurTech will continue to lead the way in making motor insurance more easily-accessible across digital platforms, while offering consumers the best value for money.

* https://www.thisismoney.co.uk/money/cars/article-8615851/Used-car-sales-halved-lockdown-brakes-1m-motor-transactions.html

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Five ways enterprises are using the public cloud

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Five ways enterprises are using the public cloud 3

By Michael Chalmers, MD EMEA at Contino

The public cloud is the most significant enabler in a generation. It’s causing a massive shift in how businesses are operating and tearing apart previous business models.

Amid challenging economic times, it’s inevitable that spending within IT is dropping. However, the cloud is the only segment that is still growing. The public cloud is increasingly becoming a central element of enterprise IT.

Contino asked 250 IT decision-makers at enterprise companies across Europe, USA and APAC within companies of over 5,000 employees about their views on the state of the public cloud within their organisation at the beginning of 2020.  Nearly all of them (99%) saw a significant technical benefit compared with on-premises.

Here are some other ways public cloud is being used by enterprises:

  1. Widely, albeit not yet business wide.

A whopping 77% of enterprises are using the public cloud in some capacity. Overall, 50% of businesses are utilising a hybrid cloud, 22% single private cloud, 20% multi-cloud, 7% single public cloud and only 1% are using only on-premises.

But only 13% of businesses have a fully-fledged public cloud program. The largest set of respondents (42%) have multiple apps/projects deployed in the cloud. 24% were still working on initial proofs-of-concept, and 18% were in the planning stages.

83% of respondents said they want to grow their cloud program. Almost half (48%) do wish to grow, but with caution, while 36% want to move as quickly as possible.

Only 4% plan to revert to on-premises but are in no rush to do so.

  1. To enhance security and compliance versus on-premises, although these are still also seen as barriers to adoption.

A massive 64% of respondents stated they find this more secure than on-premises, and only 7% see it to be less secure. 72% found it easier to stay compliant with business data in the cloud versus only 4% who found it harder.  However, 48% cited that their biggest barrier for not using the cloud was security, and 37% stated the need to remain compliant was the most prevalent blocker.

Other challenges also posed a barrier: a lack of skills, the cost to purchase and cloud-native operating models not working with existing investments made up 29-32% of responses.

19% stated that lack of leadership buy-in is the biggest barrier, reflecting that a significant number of IT departments have a need for this solution but have not been provided with the support to do so. However, relatively speaking, this was one of the least-cited barriers.

  1. For improved efficiency, scalability and agility, but vendor lock-in is still a major concern.

The top three cited technical benefits of public cloud were better efficiency, agility and scalability versus on-premises. However, 63% of IT professionals were ‘somewhat’ or ‘very much’ afraid of the commitment that can come with investing in the cloud. This is another major barrier that is preventing businesses from ​migrating to the cloud.

Only 23% are not afraid of being locked in and a meagre 5% have no fear at all. However, the fact that 77% of businesses are using the cloud shows any risk of being locked in is outweighed by the benefits of the cloud.

  1. To align IT with the business.

This is by far the most cited business benefit of the public cloud. 100% of those surveyed witnessed varied business benefits versus on-premises. Other major benefits include the ability to focus on new revenues (43%), accelerated time-to-market (43%), and increased ROI (40%).

  1. To accelerate innovation and increases cost-effectiveness.

Innovating in the cloud was quicker for 81% of respondents. What’s more, not one person surveyed said the cloud slowed down their innovation. 79% have saved money with the cloud and only 5% have found it more of an expense than on-premises.

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