Because LLMs have an ability to analyse large amounts of data quickly and generate coherent text, there is growing understanding of the potential to improve services across a range of sectors including healthcare, law, education and in financial services including banking, insurance and financial planning.

This report, which is the first to explore the adoption of LLMs across the finance ecosystem, shows that people working in this area have already begun to use LLMs to support a variety of internal processes, such as the review of regulations, and are assessing its potential for supporting external activity like the delivery of advisory and trading services.

Alongside a literature survey, researchers held a workshop of 43 professionals from major high street and investment banks, regulators, insurers, payment service providers, government and legal professions.

The majority of workshop participants (52%) are already using these models to enhance performance in information-orientated tasks, from the management of meeting notes to cyber security and compliance insight, while 29% use them to boost critical thinking skills, and another 16% employ them to break down complex tasks.

The sector is also already establishing systems to enhance productivity through rapid analysis of large amount of text to simplify decision making processes, risk profiling and to improve investment research and back-office operations.

When asked about the future of LLMs in the finance sector, participants felt that LLMs would be integrated into services like investment banking and venture capital strategy development within two years.

They also thought it likely that LLMs would be integrated to improve interactions between people and machines, for example dictation and embedded AI assistants could reduce the complexity of knowledge intensive tasks such as the review of regulations.

But participants also acknowledged that the technology poses risks which will limit its usage. Financial institutions are subject to extensive regulatory standards and obligations which limits their ability to use AI systems that they cannot explain and do not generate output predictably, consistently or without risk of error.

Based on their findings, the authors recommend that financial services professionals, regulators and policy makers collaborate across the sector to share and develop knowledge about implementing and using LLMs, particularly related to safety concerns. They also suggest that the growing interest in open-source models should be explored and could be used and maintained effectively, but that mitigating security and privacy concerns would be a high priority.

Professor Carsten Maple, lead author and Turing Fellow at The Alan Turing Institute, said: “Banks and other financial institutions have always been quick to adopt new technologies to make their operations more efficient and the emergence of LLMs is no different. By bringing together experts across the finance ecosystem, we have managed to create a common understanding of the use cases, risks, value and timeline for implementation of these technologies at scale.”

Professor Lukasz Szpruch, programme director for Finance and Economics at The Alan Turing Institute, said: “It’s really positive that the financial sector is benefiting from the emergence of large language models and their implementation into this highly regulated sector has the potential to provide best practices for other sectors. This study demonstrates the benefit of research institutes and industry working together to assess the vast opportunities as well as the practical and ethical challenges of new technologies to ensure they are implemented safely.”

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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“I’m an economic historian, before I became a central banker. Economies adapt, jobs adapt, and we learn to work with it. And I think, you get a better result by people with machines than with machines on their own,” Bailey told the BBC.

Bailey’s comments come as the latest economic assessment shows that UK businesses investing in AI are expected to see gains in efficiency and output. Utilising AI is anticipated to provide productivity benefits across multiple sectors.  

However, Baroness Stowell of the House of Lords has cautioned that the UK risks “missing out on the AI goldrush” if it does not act quickly.

A report from the Lords’ Communications and Digital Committee honed in on large language models and tools like ChatGPT. The report called for updated copyright laws and urged the government to provide clarity on AI regulation—warning too much could hinder AI development in the country.

Both Bailey and the Lords committee seem to agree that the focus should be on harnessing the upsides of AI while managing legitimate risks. 

The financial services industry also stands to gain from responsible AI adoption.

“Generative AI brings potentially exciting benefits for financial institutions. When it comes to fighting financial crime, for example, AI can improve the accuracy and speed of detection by analysing large data sets,” said Dr Henry Balani, Head of Industry & Regulatory Affairs at Encompass Corporation.

Balani emphasised however that key roles like Know Your Customer (KYC) analysts are irreplaceable for now. “It will instead accelerate existing processes and augment the work of analysts, empowering them to detect financial crime risk more quickly and comprehensively,” he added.

“The maximum value of generative AI can only be realised if banks and financial institutions have already put in place robust digital and automated processes to optimise the quality of data collated and deliver deeper customer insights. By prioritising this now, banks will be well equipped to take advantage of this new technology as it continues to evolve and mature.”

Last month, research from EXL found that around 89 percent of insurance and banking firms in the UK have introduced AI solutions over the past year. However, issues with data optimisation are often hindering their benefits.

(Image Credit: Bank of England under CC BY-NC-ND 2.0 DEED license. Cropped from original for effect.)

See also: Experts from 30 nations will contribute to global AI safety report

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The researchers surveyed executives at top UK insurers and lenders about their AI strategies and found that 44 percent have deployed AI across eight or more business functions—especially in marketing, business development, and regulatory compliance. 

Nearly 9 in 10 financial services leaders reported investing upwards of £7.9 million in AI over their last fiscal year. Over a third invested £39 million or more, exemplifying the industry’s willingness to commit major capital to AI implementation.

Despite the positive strides in AI integration, the study suggests that organisations might be overlooking the importance of prioritising their data operations. Nearly half (47%) admitted their organisations are only “minimally data driven,” raising concerns about the effectiveness of AI implementation without a solid data foundation.

“It’s clear industry leaders recognise AI’s potential, but external pressures to implement quickly can lead to unchecked investment,” commented Kshitij Jain, EMEA Practice Head at EXL. “The risk is that ensuring operations are truly data driven gets deprioritised, which can prove very costly.”

The research also identified a group of “Strivers,” representing 45 percent of respondents, who are implementing AI more narrowly across around four functions. Their focused approach has allowed them to efficiently leverage AI for cost-cutting, outperforming early AI adopters by 23 percentage points.

Additionally, over half of respondents are investing more in AI specifically due to advancements in generative AI. However, 70 percent voiced deep concerns about risks related to generative AI like potential brand damage and inaccurate data outcomes.

“The key with any AI rollout is a measured, strategic approach—getting the data architecture right, testing solutions, and training employees,” Jain concluded. “For enterprise adoption to succeed, boards must buy into AI’s capabilities and ensure investment is being used effectively.”  

A full copy of the research can be found here (registration required)

(Photo by Alev Takil on Unsplash)

See also: NCSC: AI to significantly boost cyber threats over next two years

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Dimon expressed his belief that AI has the potential to revolutionise every aspect of JPMorgan’s operations, from trading and hedging to research and error detection. He described AI as a “living, breathing thing,” capable of transforming traditional processes and augmenting human capabilities.

Dimon’s enthusiasm for AI is grounded in its current applications within JPMorgan. He revealed that AI is already extensively used in equity hedging, idea generation, and large language models.

Despite the ongoing debate about the impact of AI on employment, Dimon remains pragmatic. He acknowledged that AI will replace certain jobs, but he emphasised that technology has historically led to job displacement and this evolution is a natural part of progress.

One of Dimon’s main concerns about AI technology revolves around its potential misuse by malicious actors, especially in cyberspace. He stressed the importance of establishing legal safeguards to prevent the misuse of AI.

Despite these concerns, Dimon remains optimistic about the positive impact of AI on the workforce and society. He highlighted the benefits of other technological breakthroughs, many of which can be further enhanced using AI.

“Your children will live to 100 and not have cancer because of technology, and they’ll probably be working three days a week. So technology’s done unbelievable things for mankind,” said Dimon.

Dimon outlined JPMorgan’s proactive approach to potential job displacement caused by AI implementation. He expressed the firm’s commitment to supporting employees who might be affected, stating that they plan to redeploy displaced workers in local branches or different functions within the company.

Dimon’s forward-thinking approach highlights the transformative power of AI in shaping the future of finance and other industries. However, it also reiterates the need for consideration of how to minimise negative impacts such as job displacement.

(Image Credit: Stuart Isett/Fortune Global Forum under CC BY-NC-ND 2.0 DEED license)

See also: Cyber Security & Cloud Expo: The alarming potential of AI-powered cybercrime

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Gensler, who has been vocal about the risks and challenges posed by the cryptocurrency industry, now believes that AI is the technology that “warrants the hype” and deserves greater attention from regulators.

Gensler’s interest in AI dates back to 1997 when he became intrigued by the technology after witnessing Russian chess grandmaster Garry Kasparov’s infamous loss to IBM’s supercomputer, Deep Blue.

As an MIT professor, Gensler delved deeper into the study of AI, co-authoring a significant paper in 2020 that highlighted the risks posed by deep learning in the financial system.

His concern over the potential implications of mass automation using AI in the finance sector has led him to reevaluate regulatory approaches. Gensler believes that while AI can bring immense benefits to financial firms and their clients through enhanced predictive capabilities, it also carries significant risks that need to be addressed.

“Mass automation can have cascading implications for trillions of dollars in assets that trade on markets overseen by the SEC,” warns Gensler.

One of Gensler’s key concerns is the potential use of AI to obscure responsibility and accountability when things go wrong. Coordinating AI models among major trading houses could lead to increased market volatility and instability, a phenomenon that current regulatory regimes might not be equipped to manage.

As a result, Gensler has taken a proactive step by proposing one of the first regulatory frameworks for AI in the finance industry. His proposal requires trading houses and money managers to carefully evaluate their use of AI and predictive data to identify any conflicts of interest, especially when the interests of clients clash with company profits.

However, this shift in focus does not mean the SEC is easing its crackdown on cryptocurrencies.

Under Gensler’s leadership, the SEC has actively pursued legal action against major crypto firms like Ripple, Binance, and Coinbase. Several lawsuits are currently pending, signalling that the SEC remains committed to enforcing its actions against cryptocurrency companies that engage in scams and fraudulent activities.

Gensler’s emphasis on AI comes at a crucial time when the technology is making rapid strides in automating various financial processes.

While AI holds tremendous promise in revolutionising the industry, its unchecked growth could also lead to unforeseen challenges. By directing the SEC’s attention towards AI, Gensler aims to strike a balance between promoting innovation and safeguarding market integrity and investor interests.

(Photo by Petri Heiskanen on Unsplash)

See also: AI Act: The power of open-source in guiding regulations

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The UK is Europe’s AI leader. Indeed, behind the US and China, the country’s tech sector overall has the third-highest amount of VC investment in the world – more than Germany and France combined – and has produced more than double the number of $1 billion tech firms than any other European country.

Gerard Grech, CEO of Tech Nation, said:

“As a nation uniquely positioned between two economic powerhouses, the US and the EU, we must harness innovative regulation that will enable us to propel ourselves as an international hub and leader for AI, quantum computing, and deep tech.

This is a critical step towards creating a distinctive, value-driven tech ecosystem in the UK, setting us apart from other tech hubs.”

To support British startups, an ‘AI Sandbox’ was announced by the chancellor. The sandbox features a number of initiatives designed to encourage AI research and investment.

Among them is a prize pot containing millions of pounds. £1 million will be up for grabs every year over the next decade for the best AI innovations created by teams and individuals.

Ludovico Lugnani, Technology Solicitor at BDB Pitmans, comments:

“Following yesterday’s news of Open AI’s launch of its upgraded GPT-4 chatbot, the Budget’s announcement as to the creation of an AI sandbox offers a promising outlook for the UK to speed up the arrival of AI products to market.

As part of this, particular emphasis should be placed on providing effective guidance as to the implications of copyright law on generative AI applications following the recent claim by Getty Images against Stability AI over breach of copyright.”

Elsewhere, £2.5 billion is being ploughed into advancing quantum computing. The powerful machines will enable a literal “quantum leap” in AI.

“The power that AI’s complex algorithms need can be provided by quantum computing,” the chancellor told the Commons.

£900 million is also being invested to create an exascale supercomputer that will be several times more powerful than the country’s biggest computers and advance not just AI research, but also science, healthcare, defense, weather modelling, and more.

“[The supercomputer] should be a huge boost to the UK’s ability to support cutting-edge research in areas requiring complex modelling and simulations, such as climate change, pharmaceutical development and hi-tech engineering,” commented Nick White, Partner at law firm Charles Russell Speechlys.

Only one exacomputer is currently known to exist. The computer, known as Frontier, is housed at the Oak Ridge National Laboratory in Tennessee, United States.

Other relevant announcements in the Spring Budget are targeted less at the AI industry specifically but aim to solidify the UK’s ranking as the second-best country after the US to invest and launch a business.

Under the ‘Full Expensing’ plans, companies investing in R&D and IT equipment to boost growth will benefit. Every pound a company spends on new IT equipment and machinery can be deducted in full from taxable profits.

The independent OBR (Office for Budget Responsibility) says the measure will increase business investment by three percent every year. The chancellor has committed to the measure for the next three years but intends to make it permanent “as soon as we can responsibly do so”.

Furthermore, smaller businesses will also have an increased annual investment allowance of up to £1 million. This means that 99 percent of SMEs will be able to deduct the full value of all their investments from taxable profits.

Matt Hammond, Founder of Talk Think Do, said:

“I wholly welcome the tax savings on IT investments, research and development as announced in today’s budget. In recent years, Talk Think Do has benefitted substantially from the R&D relief and as a result, we have been able to reinvest the extra cash directly into hiring sector-leading talent.  

R&D relief has helped to accelerate our expansion by over 750 percent in just four years. We are a successful example of how the relief has encouraged greater innovation in UK businesses and has unlocked significant growth opportunities. Today’s update benefits the economy at large and so I am glad to see this has been considered in the budget.”

The creation of 12 investment zones is set to further boost the UK’s tech credentials and spread opportunity across the country.

Eight have been announced in England and will be around research institutions in the East Midlands, Greater Manchester, Liverpool, North East, South Yorkshire, Tees Valley, West Midlands, and West Yorkshire.

Four more will be in Scotland, Wales, and Northern Ireland. These investment zones, based in the UK’s devolved administrations, will be announced by the end of the year.

Rikke Wichmann Bruun, CEO of MRM UK, commented:

“The announcement of investment into technology – including green technology – through the 12 investment zones presents a great opportunity for businesses and brands in the UK.

The ambition to transform Britain into the ‘next Silicon Valley’ also echoes new research conducted by our agency which found that Brits are the most optimistic about technology’s potential, in comparison to other Europeans.”

Cambridge, Oxford, and London – the so-called ‘golden triangle’ – are often seen as Europe’s closest rivals to Silicon Valley. Each city benefits from world-leading universities and research institutions that help to produce innovative startups and address global talent shortages.

The three cities are based in southern England and have historically benefited more from investment compared to the rest of the UK. Other cities – including Edinburgh and Manchester – have attracted increased investment in recent years, but it’s hoped the new zones will close the gap and unlock the potential across every region of the UK.

“Predictions that inflation will fall to 2.9 percent by the end of 2023 will be very welcome and there were a range of measures announced to boost the economy, including 12 new regional investment zones and a new policy to replace the ‘Super Deduction’,” said Stuart Haynes, Corporate and Commercial Partner at law firm Aaron & Partners.

“There are some fantastic tech innovators in this country and it’s pleasing to see the chancellor really get behind this sector to be a catalyst for economic growth.”

(Image Credit: Zara Farrar / HM Treasury under CC BY-NC-ND 2.0 license)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The event is co-located with Digital Transformation Week.

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While making some recovery in the past couple of weeks, the crypto market has suffered a horrid couple of months. Bitcoin crashed around 50 percent between November 2021 and the end of January 2022. As of writing, the largest cryptocurrency remains around 37 percent down while many “altcoins” have still lost over 50 percent of their value.

DynaSets combine AI algorithms with professional hedge fund traders in a bid to maximise profits and minimise losses in a notoriously volatile market (although the same could be said for the “stonk” market in recent weeks…)

Marcello Mari, CEO of SingularityDAO, said:

“I’m impressed by the preliminary results from the beta version of our DynaSets.

Over the next month, we’ll be further empowering our traders with more tools including the ability to short the market and execute trades with leverage.

We’ll also be launching real machine learning tools that have never been used in the crypto market before.”

Since the beta launch of DynaSets on 20 December 2021:

• Bitcoin DynaSet shows 10.3% better performance over just “hodling” Bitcoin
• Ethereum DynaSet shows 12.59% better performance over hodling Ethereum

“As we move closer to our 1.0 product offering, we will further improve on the performance we demonstrated with our beta. That is, deliver further and further automation with a constant focus on crypto asset protection and AI safety,” commented Chris Poulin, CTO.

The combination of SingularityDAO’s AI algorithms and professional traders were reportedly able to identify potential triggers for the crypto decline including the Fed’s hawkish policy shift, electricity price rise, and political instability of the world’s second-largest producer of bitcoin, Kazakhstan.

As of writing, two DynaSets have been launched for the two largest cryptocurrencies: Bitcoin and Ethereum.

In 2020, full-stack AI solution SingularityNET announced that it was collaborating with Cardano due to Ethereum’s issues.

Earlier this month, Goertzel announced HyperCycle—a lightweight layer 2 architecture designed to enable inexpensive, high-speed, large-scale on-chain execution of microservices and specifically designed to optimise AI-related processes.

HyperCycle leverages TODA/IP ledgerless protocol, SingularityNET’s Proof of Reputation, and Cardano’s EUTxO model and Hydra sidechain framework.

Cardano seems a prime candidate for one of the next DynaSets that SingularityDAO launches.

(Photo by Jared Schwitzke on Unsplash)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo. The next events in the series will be held in Santa Clara on 11-12 May 2022, Amsterdam on 20-21 September 2022, and London on 1-2 December 2022.

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Wealth management is a data-rich industry that has traditionally relied heavily on human data rekeying processes. Many thousands of person-hours are devoted annually at these firms to repetitive, mundane data inputting tasks and simple data interrogation and cleansing. Data can be about clients or markets and using the two sources intelligently is a principle of the wealth management business model. But the latest artificial intelligence (AI) technologies will change this model.  

Like many other organisations, asset management firms often have a segregated structure (apart from those areas that need to be separated under financial regulations). And with finance being such a broad and deep field, employees build their experience or specialise in subfields. This partitioning is also true of the niche legacy applications used in firms that have developed over time. For example, one application can be used by the performance team to calculate portfolio returns, and another by the client reporting team to produce reports that contain information about those returns.  

While understandable, these silos mean that the systems often don’t interact and use incompatible data formats. Thus, employees become the go-between manually duplicating data between systems which is inefficient and results in human error. Further manual work is often then required to correct mistakes. Client deadlines are not met, and SLAs breached, resulting in immediate monetary loss or penalties to the wealth management firm.  

So, how can AI help? The most significant gains are to be had in the intelligent automation field. Technologies such as robotic process automation (RPA), process mining, conversational AI, and automated decision-making can deliver real outcome improvements. 

RPA software can be used to transform legacy systems without replacing or redeveloping them. The latest zero-code machine learning tools mean a person without coding skills can build and teach a software robot to do simple tasks. They can assume the role of data scientist to automate even complex processes. For example, a software robot can read performance returns from one application and enter them into the client reporting application without error. Not only does this create massive time savings and improve operational efficiency, but it also frees up employees from the repetitive tasks that they don’t like to focus on more strategic work.  

Communications mining tools are also set to revolutionise the industry. Deep learning technology can now convert unstructured communications (emails, calls, chats, notes) into structured data in real-time. It does this by using unsupervised learning algorithms on a source of unstructured data, e.g. an email inbox. These then create clusters of similar communications (emails in this example) to be presented back to the user. The software will cleverly group emails it thinks are about the same thing. It then asks the user to ‘label’ the cluster to learn what conversation these emails are covering. The tool will then present back more examples to be confirmed and continuously develop its learning.  

With machine learning, the user can also label data points within emails by simply highlighting an entity and giving it a name. This is where automation possibilities are unlocked. Users can educate the machine learning tools to identify what a Sedol, ISIN, gross return, or net return looks like from within the body of an email. Company name, client name, fund name and more. The tool will then find each entity’s nuances to present back to the user for affirmation or correction.  

IT requests are another area where AI can help create enormous efficiencies for organisations. Using metadata gathered from communications mining, wealth management firms can see where automation benefits could best be realised. For example, a high volume of IT requests relate to onboarding new clients and new employees. A machine learning product can be trained to understand an onboarding request coming into the IT mailbox and recognise and pick out relevant elements.   

Using RPA robots then communicate to relevant teams and systems, setting up the client or worker correctly. The whole process can be automated using a suitable workflow tool that manages how each process is handled and by whom.  

AI can transform how the wealth management industry operates by reducing the person-hours spent on mundane, repetitive tasks. As a highly competitive industry, firms that embrace AI will keep a competitive advantage by driving efficiency and customer service improvements. 

Using RPA, robots then communicate to relevant teams and systems, setting up the client or worker correctly. The whole process can be automated using a suitable workflow tool that manages how each process is handled and by whom.  

For the wealth management industry that spends so many man-hours on mundane, repetitive tasks AI has the ability to transform the way they operate. As a highly competitive industry, those firms that embrace AI will keep a competitive advantage by driving improvements in efficiency and customer service.

(Photo by Nick Fewings on Unsplash)

Find out more about Digital Transformation Week North America, taking place on November 9-10 2021, a virtual event and conference exploring advanced DTX strategies for a ‘digital everything’ world.

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For some time now, the sector has also been at the forefront of another top technology trend. As they evolve to become the interaction hub of the digital enterprise, contact centres have been deploying AI-technologies at pace. Leveraging image recognition, natural language processing (NLP) and data analytics contact centres are now able to predict call volumes, enable new automated customer self-service channels, and boost the performance of contact centre agents.

Unsurprisingly, when the pandemic hit, contact centres were able to pivot at speed to cope with the ‘new-normal’. So much so, that other industry sectors have been inspired to deploy many of the AI technologies pioneered by contact centres to respond more effectively to the operational realities of today’s post-COVID world.

Transforming omnichannel retail

In the face of repeated national lockdowns, retailers have had to evolve their omnichannel service offerings fast to accommodate rapidly shifting consumer shopping habits. To optimise on-demand home deliveries and click-and-collect pickups from stores that are now operating as virtual warehouses, they had to utilise AI to integrate stock data from across their store and distribution networks. They are also using AI to auto-detect and proactively avoid inventory scarcity issues and aggregate and analyse consumer demand to accurately forecast the optimal product assortments and manage supply chain issues.

To further enhance the online shopping experience and make it more personalised, many retailers are investing in AI tools that enable customers to create a virtual model of themselves using their mobile or laptop camera. This offers customers the ability to ‘try on’ items like make-up, hair colour and clothing from the comfort of their home or create interactive 3D ‘photos’ of rooms to better visualise how furniture and accessories might look in their own spaces.

Revolutionising healthcare service delivery

The outbreak of coronavirus has helped to fast track a number of NHS initiatives designed to transform patient care and make it more predictive, preventative, and personalised. Intelligent automation (IA) – a technology similar to AI that combines robotic, intelligent and autonomous systems – is providing a foundation for this accelerated digital transformation. From supporting faster and more accurate disease diagnosis, to the more effective management of beds and staff resources and ensuring the NHS is better prepared and more responsive to future pandemics, IA is being leveraged to bring life-saving innovation across the health service.

But it’s not just clinical and disease management pathways that are benefiting. The NHS has also taken a pioneering approach to applying this technology to bolster the performance of its NHS 111 online and telephone service. Utilising automatic call routing technology has enabled the service to respond faster and more effectively to patients, prioritising at risk patients with complex or long term health conditions to the right person first time together with all their patient records and details.

These technologies also played a key role in enabling all healthcare providers to collaborate in critical decision making, in conjunction with government bodies, around the best way to deploy resources in emerging coronavirus hotspots. Similarly, digital agents are being used to streamline the management of patient prescriptions, online video bookings with GPs and clinicians and more.

Financial services deploy AI to support new ways of working

COVID-19 accelerated a rapid uptick in the adoption of call centre and channels by customers. Indeed, some banks reported a 400% increase in inbound calls in March 2020 alone. In response, banks and other financial institutions had to innovate at speed, turning to intelligent automation solutions to upscale their operations and streamline previously human intensive and error-prone processes like onboarding, know-your-customer, and anti-money laundering checks. 

To deliver improved customer support, they’ve also utilised AI-powered virtual assistants to automate responses to general queries – such as resetting passwords – and generate a deeper understanding of each customer’s context, behaviour needs and preferences so they can provide personalised recommendations and more streamlined customer journeys.

With thousands of employees across call centres, trading departments and other teams forced to work from home, financial services organisations have turned to AI to mitigate risks and ensure services continue to be delivered in a compliant way. 

Operational agility – it’s the future

Organisations in every industry sector are now taking advantage of AI and IA to optimise performance, streamline processes, deliver enhanced and more personalised services to customers, and uncover insights that can inform strategic decisions.

Providing the resilience that is needed to stay one step ahead of evolving market and consumer demands and disruptive events, AI technologies are now going mainstream and transforming the world of work as we know it.

(Photo by Matt Artz on Unsplash)

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Using AI, public sector organisations can both identify and rectify potential issues before they become a problem, saving valuable money in the process and ensuring these precious public funds are being administered and used effectively. 

Introducing technology 

The latest warnings from the National Crime Agency suggested that coronavirus-related fraud could end up costing the taxpayer £4bn as a result of criminals seeking to capitalise on the COVID crisis. Fraudulent activities are ranging from an increase in company registrations to benefit from the Bounce Back loan, to misuse of the job retention (furlough) scheme; but how can government departments not only identify fraud and error, but also recoup the lost funds?

There was a need for these schemes to be deployed extremely quickly, and the government succeeded in that. But the speed at which the funds became available also created numerous opportunities for both systematic error and fraud. The peak of the financial crisis might be behind us, but businesses and individuals are still in need of ongoing financial support, so it is now essential for the public sector to learn valuable lessons to target fraudulent activity and minimise the potential loss of public funds in the future. 

Government now has an opportunity to strengthen internal controls and make sure the use of public funds is appropriate and technology has a key role to play here in detecting and preventing future fraud and error. 

AI in audit 

In order to remove the current levels of fraudulent activity and gain valuable insight into the primary areas to address, the public sector must use an intelligent, data-led approach. By using Artificial Intelligence (AI) within public sector IT systems, errors can be detected, fraud or mismanagement of funds can be spotted and the entire process can be streamlined to prevent further problems. 

Great work is currently being undertaken by HMRC, which has used its extensive experience in identifying and tackling tax fraud to address the misuse of the furlough scheme. If public money is to be safeguarded in the future, it is likely that a central government initiative will be required; other public sector bodies, especially smaller local authorities, are less likely to have the resources or skills in place to undertake the analysis required.

The use of data resources is key here as the government holds a vast amount of data; although, it is possible that the delivery speed of COVID-19 financial and funding responses will have created gaps in data collection which will need a rapid resolution. The priority must be to identify these gaps in existing data and simultaneously use Machine Learning (ML) to reveal anomalies that could be as a result of either systematic error or fraud.

Additionally, this insight from ML can also provide public sector bodies with a chance to move towards the use of predictive analytics to improve control and move away from a retrospective review. By developing an understanding of the key indicators of fraud, the application process can automatically raise an alert when a claim looks unusual, minimising the risk of such claims being processed and therefore drastically reducing the risk of fraud.

Invaluable insight 

It might seem daunting for many public sector bodies, but it is essential to take these steps quickly. Even at a time of crisis, good processes are important – failing to learn from the mistakes of the past few months will simply compound the problem and lead to greater misuse of public funds. The public sector, businesses, and individuals need to learn how to operate in this environment, and that requires the right people to spend time looking at the data, identifying problems and putting new controls in place. With an AI-led approach, these individuals will learn lessons about what worked and what didn’t work in this unprecedented release of public funds. And they will gain invaluable insight into the identification of fraud – something that will provide on-going benefit for all public sector bodies.

(Photo by Allef Vinicius on Unsplash)

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