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The role of Artificial intelligence in compliance at banks

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Sujata Dasgupta, Global Head – Financial Crime Compliance Advisory, Tata Consultancy Services

 

There’s not a financial institution across the globe that will be a stranger to money laundering, fraud, and other financial crimes. In the UK, institutions have witnessed a significant rise in financial crime since the pandemic, with analysis by money.co.uk of the fraud and cyber-crimes reported to Action Fraud finding that £2.4 billion was stolen in 2021, 174% more than the previous year. And these are just those crimes that are reported– in reality, the amount lost to criminals could run to tens or hundreds of billions of pounds per year.

With the frequency and severity of such crimes on the rise, they increasingly pose a significant challenge to banks and financial services firms. Many banks have been struggling to tackle longstanding financial crime compliance issues due to poor data quality, fragmented compliance platforms, high false alert volumes, and high operational costs.

But the biggest problem is that the financial crime compliance units of these financial institutions still rely mainly on heavy manual processes. Their key reason for their cautious approach in the utilisation of AI and automation has been uncertainty about technology. Do regulators approve machine-based decision-making, and is machine learning logic fair in identifying suspicious activities?

This uncertainty highlights a clear need for the proper utilisation of technology in financial crime compliance. Financial crime compliance functions are responsible for monitoring account transactions and customer behaviour for money laundering, terrorist financing, bribery, corruption, and fraud. The current systems and processes of the financial institutions mean that these tasks involve heavy manual routine measures that take up a significant portion of staff’s working time.

Regulatory technology, or RegTech, could make handling these routine tasks more efficient. This tech can utilise machine learning, advanced analytics, NLP, dynamic biometrics, network graphs and other forms of AI, which can take responsibility for routine tasks such as data collation and processing, as well as some other everyday responsibilities. This can free up significant portions of analysts’ time for more complex, cognitive work such as in-depth investigations into potential crimes, as well as other tasks that require data-driven judgement and decision-making.

Lack of access to information on criminal activities outside national boundaries has hindered efforts to combat banking fraud and compromised anti-money laundering and financial crime compliance actions taken by banks. While the need for collaborative action in fighting financial crime is evident, financial crime intelligence sharing has hit a roadblock given restrictions imposed by data privacy regulations. As usual, technology has come to the rescue – privacy-enhancing technologies (PET) offer a way to share data while protecting privacy. Using PETs can help financial institutions to understand suspicious patterns of behaviour through financial information sharing and analytics whilst preserving the privacy of individuals.

Internationally, governments are just now starting to utilise PETs. For example, in June 2022, the UK and US governments collaborated to develop PETs to tackle digital financial crime and enable data sharing better across borders to prevent money laundering attempts.

There has been some progress in using technology to fight financial crime on a national level also. In 2019 the Bank of England announced that it would be working to encourage the introduction of artificial intelligence-based RegTech among UK banks. It pledged to launch a review in consultation with financial institutions to increase the use of RegTech over the next decade, to reduce the burden on them and improve the quality and effectiveness of their data. UK’s Regulator, Financial Conduct Authority (FCA), has encouraged FIs to use advanced technology to prevent and detect fincrimes, providing sandboxes for RegTech solutions development and validation. FCA has been organising Tech Sprints every year to foster innovation in this space.

Some banks in the UK have already started adopting AI-powered solutions in fighting financial crime, while others are yet to explore advanced technology in this space. As financial crimes quickly grow more complex, adding more people to compliance functions alone may not help in disrupting them. A strong defence requires a combination of people, processes, and platforms. Processing huge volumes of data to uncover sophisticated criminal networks and illicit money trails in financial institutions must necessarily utilise AI.

In 2021, the European Commission announced guidelines on ethical AI, pursuant to which artificial intelligence solutions will be classified as a minimal, limited, high or unacceptable risk. Recently the UK Government also announced plans to publish a national strategy on responsible AI adoption. These types of guidelines based on regulation will support increased AI usage even in compliance functions.

In the UK, people have trust in banks and financial institutions. The only way for the financial sector to maintain and grow this trust is to keep up with the development of technology which can help fight financial crime. Financial institutions must step up and proactively seek technological and AI solutions to deal with increasingly sophisticated criminals and hackers. The time for action is now!

Banking

Augmented automated underwriting and the evolution of the life insurance market

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By Alby van Wyk, Chief Commercial Officer at Munich Re Automation Solutions

 

It’s almost inevitable. Spend your working life identifying, analysing, quantifying and ascribing monetary value to risk, and you’re likely to have a fairly strong aversion to it. Or more accurately, an aversion to undertaking new endeavours with inadequately understood consequences. The insurance industry is, on any number of levels, the very definition of risk-averse.

And yet, for all the commentary suggesting otherwise, insurance still has an appetite for innovation. If the insurtech sector is any indication, then an interest in and requirement for new solutions is being recognised and slowly addressed.

Declan O’Neill

It may not employ the language of disruption that runs through the wider fintech market, it may be short a few unicorns and unable to boast some of the record-breaking funding rounds, but a quiet tech evolution has been building in insurance nonetheless. Hence the advent of automated underwriting facilitated by more advanced algorithms and data analysis.

Where insurtech does overlap with its more vocal fintech counterparts is in the greater use of artificial intelligence (AI) and machine learning to solve age-old problems around data analysis and interpretation.

It’s about five years or so since AI first became a topic of conversation in insurance. Since then, despite the intensity of the debate, it has often felt like a reality that is always just over the horizon – a destination that kept moving even as more and more efforts were directed towards it.

But recent research suggests that the journeys made so far have not been in vain. We are at a point where embracement of AI is about to step up a gear. The global value of insurance premiums underwritten by AI have reached an estimated $1.3 billion this year, as stated by Juniper Research; but they are expected to top $20 billion in the next five years. As a destination, it is closer and more attainable than ever before.

However, AI is not an island. Its promise of $2.3 billion in global cost savings to be achieved through greater efficiencies and automation of resource-intensive tasks will not be achieved in isolation.

AI remains part of a more complex ecosystem of data gathering and analysis. It can apply new technologies to get the best out of the already established and still-emerging data sources that feature in underwriting offices around the world. It emphatically does not require these existing investments to be ripped out, replaced or downgraded.

It is more helpful therefore to see AI as the differentiating factor in the latest generation of insurance IT: augmented automated underwriting, or AAU for short.

AAU gives underwriters the ability to spot patterns and connections that are, frankly, either invisible to the human eye or which take normal, human-assisted processes unfeasible amounts of time and resource to identify.

Whereas earlier generations of automation were able to pick up the low-hanging fruit of insurance markets – the individuals whose driving history fit into clearly delineated boxes, for example – AAU can take into account all of the rich complexity of the human experience. It can spot the nuances and individualities that populate the life market, for example, and translate those into accurate policies.

That’s good news for both underwriters and their customers. AAU can significantly reduce the need for separate medicals, repeated questions, lengthy decision-making processes, and drastically increase the speed at which a potential insurer can get a quote and cover – while continually improving the way risk is calculated and managed.

It can make sure the decision-making process remains in the hands of underwriters rather than IT departments, enabling them to set and update the rules and parameters as befits their preferred business model. It consequently makes advanced, complex and precise decision-making available to a broader range of underwriting businesses – which is good for those businesses, good for customers and ultimately good for the entire industry.

AAU – augmented automated underwriting – is an example of the realisation of AI’s promise. As such, it’s set to become one of the key talking points and disruptive technologies of the insurance industry. And this time, AAU is both a journey and destination that all progressive insurance organisations need to be considering for their future operations.

 

 

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Banking

ESG in the finance and banking industry – are you ready?

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By Julian Moffett, CTO BFSI, EDB

 

Environmental, Social and Governance (ESG) has soared towards the top of banking, financial services, and insurance (BFSI) and other boardroom interests. Organisations everywhere know they need to take ESG and greenhouse gas emissions (GHGs) seriously not only because it is the right thing to do for the future of the planet or because it can help attract and retain talent, but also, because failing to do so may pose a risk to the economic value of their businesses and encourage probes by governments, watchdogs and non-execs. However, complying with complex reporting and going the extra mile to actually deliver on the goals of the rules is a challenge in many ways, not the least of which is in achieving the required excellence in data management to underpin strong reporting on ESG.

 

What is ESG? 

Julian Moffett

ESG is an umbrella term that covers a broad gamut of activities. Gartner defines ESG as “…a collection of corporate performance evaluation criteria that assess the robustness of a company’s governance mechanisms and its ability to effectively manage its environmental and social impacts.”

The CFA Institute describes the environmental element as focusing on “the conservation of the natural world” and includes measuring “climate change and carbon emissions,” “air and water pollution” and “biodiversity” among many other measures. Social considers “people and relationships” looking at areas including “customer satisfaction,” and “gender and diversity.” Governance covers “standards for running a company” and analyses factors such as “board composition,” “audit committee structure” and “audit committee structure.”

 

Status of the current regulatory environment

There are many bodies proposing rules to formalise ESG monitoring and seeking to ensure corporate compliance. Some example groups, frameworks and bodies:

  • The Task Force on Climate-related Financial Disclosures (TCFD)
  • Streamlined Energy and Carbon Reporting (SECR)
  • The International Regulatory Strategy Group (ISRG)
  • The Sustainability Finance Disclosure Regulation (SFDR)
  • The International Sustainability Standards Board (ISSB)
  • The Sustainability Accounting Standards Board (SASB)
  • Sustainable Development Goals (SDGs), the Global Reporting Initiative (GRI) support efforts such as the US SEC’s Climate and ESG Task Force.

Financial services organisations are very aware that the current regulatory landscape is far from mature (and will continue changing) both in terms of alignment between bodies and also with regard to when the new rules will come into effect. At the of time of writing:

  • The requirement for Scope 2 disclosures (see below for description) for the Sustainable Finance Disclosure Regulation (SFDR) will likely come into effect in 2023
  • A proposed Corporate Sustainability Reporting Directive (CSRD) should be agreed by the European Parliament this year for implementation in 2024 to report on performance in 2023.
  • Meanwhile, the SEC has just released its proposed rules for climate-related disclosures, which,if passed in legislation, may come into effect as early as year end 2022.

 

Reporting Obligations 

Reporting can cover a wide range of areas covering energy consumption, GHG emissions, water consumption and waste management to health and safety, labour rights, diversity and inclusion to ethical conduct, and even areas such as appropriate executive compensation.

While the regulatory reporting obligations are not yet finalised, the expectation is that compliance may prove to be an onerous task. For example, organisations are under pressure to monitor carbon emissions but even so-called Scope 1 emissions (those that come from owned or controlled emissions) can be hard to track. Factor in Scope 2 (indirect emissions such as purchased power) as well as Scope 3 emissions from up and down value chains, and the reporting task at hand is difficult indeed.

To measure, monitor and manage in addition to staying on the right side of rules, organisations need to have excellent data management fundamentals, strong reporting tools and a new class of applications, which also have the agility to adapt to rapidly changing regulatory demands. Data will be used both to support decarbonisation measures but also to identify where there are disclosure gaps. It was telling that when the SEC issued a press release on its Enforcement Task Force, it specifically referred to data:

“The task force will also coordinate the effective use of Division resources, including through the use of sophisticated data analysis to mine and assess information across registrants, to identify potential violations.”

Having reliable data comply with emerging rules isn’t the only essential requirement for organisations. Institutions need such data to understand where they are in their journey to sustainability, so that they can set sensible targets and track progress against them. Organisations will have to cover the data trifecta of availability, management and transparency. Many organisations may be stuck in the early stages of managing ESG, overly relying on manual processes, spreadsheets and email. But their target should be to get to real-time data insights that are easily visualised, understood and shared. As a foundation, BFSIs need to capture, manage and securely share data reflecting consumption and safety to emissions, financials and data from surveys measuring results against ESG targets. Data emanating from ERP and other back-office systems, performance data from third-party associates, media and social network coverage, spatial/geolocation systems and beyond should also be factored in.

 

Actually reducing GHGs

Organisations are using a wide variety of ways to reduce emissions and improve their footprints from using renewable energy sources to making secondary use of energy; for example, in the case of one university, this is done through capturing data centre heat in hydroponics. For IT, making broader use of multitenancy in cloud computing and hosting services is a popular way to reduce emissions. Not only do these large data centres offer an economy of scale, they also tend to be state of the art in their use of renewables and highly efficient hardware and other infrastructure. Gartner, in an article titled The Data Centre Is Almost Dead, says it expects 80 percent of enterprises will close in-house datacenters by 2025. For me, the jury is out on this one but an interesting one to monitor going forward.

 

Conclusion

We are at the start of a very significant inflection point in regulatory and consumer expectations around ESG. BFSIs should be under no illusion that momentum is building rapidly in terms of having to address strict reporting requirements and implement strategies to reduce GHGs.

However, we also see this as a time of positive change. As the leading provider of Postgres, EDB is excited to help organisations further their ESG goals as the journey unfolds. We are closely monitoring the implications of ESG regulations as they will give rise to a new class of applications and drive adoption of green data centres. We see OSS, including Postgres, as playing a key role in this shift as often the movement to private and public cloud helps accelerate application modernisation and enables displacement of outdated incumbent technology (including database) platforms. As the leading provider of Postgres, EDB is excited to help organisations further their ESG goals as the journey unfolds.

 

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