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Technology

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.

Technology

Creating a culture of cybersecurity in Financial Services

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Creating a culture of cybersecurity in Financial Services 1

By Martin Landless, Vice President for Europe at LogRhythm

As the financial services sector increasingly moves online and reaps the benefits of the modern digital economy, the sector has become an even more tantalising target for cybercriminals.  Financial data is among the most lucrative data types for cybercriminals, going for high prices on the Dark Web or used to access accounts, copy payment cards and make fraudulent purchases.

For any business which suffers a successful cyberattack, the consequences can be severe. A halting of business processes whilst the business gets up and running again can impact the bottom line, negative media attention can dent customer confidence, and the potential for a large General Data Protection Regulation (GDPR) fine can derail existing plans for business growth.

These consequences will be front of mind for financial services leaders now, as the sector has found itself in the crosshairs even more so during the current pandemic. Recent data from VMWare indicates that cyberattacks against the financial sector increased by 238 per cent from February to April 2020, with cybercriminals looking to take advantage of the tumult to steal valuable data.

Although financial services institutions find themselves under attack more frequently than ever, it is still possible to remain at the forefront of the digitalisation of the industry and remain secure. Doing so relies on a three-pronged approach, with people, processes and technology all working in concert towards ensuring cybersecurity. Through a holistic approach, a culture of cybersecurity can be created that protects institutions.

Security maturity

Given the sensitivity of the data they manage, financial services organisations must have a mature security operation model in place to deal with threat actors. Security operations maturity is measured based on two variables: mean time to detect (MTTD) threats and mean time to respond (MTTR) to them.

A reduction of both MTTD and MTTR is crucial to ensuring cyberattacks are halted earlier in the threat lifecycle, and is reliant on technological solutions which allow for the automation of workflows. This frees up vital time for security teams to focus their attention where it is most needed. Indeed, a recent survey of security professionals and executives found that 47 per cent[1] of those surveyed felt that they needed increased security teams, so anything that can maximise the effective time of existing cybersecurity personnel is a huge benefit. Visibility across networks and systems is also key, as cybersecurity teams must be able to immediately see shifts in behaviour in the network to recognise imminent threats as they arise.

Although technological innovation in security response is a strong foundation for an effective culture of cybersecurity, this must be complemented with processes and security training for employees.

Ensuring cybersecurity is a board-level issue

It is the responsibility of the CISO and the security team which works under them to ensure that security is front of mind for all employees. A chain is only as strong as its weakest link, and it only takes one employee falling victim to a phishing email to compromise a business. CISOs may be senior figures in a business, but they need the support of the rest of the C-suite to fulfil their goals. At the board level, CISOs must ensure that executives are aware and fully understand the challenges security teams encounter day to day and the longer term[2].

Martin Landless

Martin Landless

This then becomes a matter of communication rather than technology. One potential means of communicating security posture to the board is by focusing on the benefits and return on investment an effective security posture can entail. Additionally, a CISO can furnish a high trust environment through partnering a member of the board with the security team.

This partner can articulate perspective to the team from a purely business standpoint, allowing the team to produce intelligence to the board that exhibits the business value of the security operation centre’s (SOC’s) methods and goals. This collaborative approach will encourage the understanding security teams have for business goals and the board’s understanding of security necessity.

Growing security alongside the business

One area of understanding between security team and leaders that should be nurtured is the impact of business growth on security. Although business growth indicates that a business is in robust health, it also facilitates multiple avenues through which a company can come under cyberattack.

Firstly, don’t assume cybercriminals aren’t keeping an eye on the markets and on the business pages. They’ll be aware of a company’s raised profile and whether they’re now a more lucrative target – or not. Positive business events like mergers and acquisitions can also present opportunities for cybercriminals. On a tech level network and security systems of different companies may be in the process of being migrated and integrated, and on a more human level, new staff, as yet unaware of the security protocols of the company they’re joining, can be targets.

It’s important then that security teams ensure each new employee is vetted, safely added to the system and trained on appropriate security protocol. In the case of acquisitions, security teams must effectively monitor new structures that are added to the network, and third-party connections with whom they are not yet familiar. A Gartner study earlier this year identified third-party cybersecurity risk as a key concern for half of legal and compliance leaders.

This is all easier said than done however, and key to this issue is security budget, and it is here board-level support is important. Security budgets are often determined in advance and follow two common pricing models used by security vendors: the user-based model and capacity-based model. In the face of growth, both are fixed, and may leave security teams making difficult decisions as to where they safeguard their organisations.

Executives should instead look for security vendors which offer a subscription-based model. This offers the guarantee of scalable security at a determined rate, which will greatly alleviate the stress felt by security teams in what often should be an exciting time for an entire organisation.

Changing security budgets to better facilitate the work of SOCs represents a culture of cybersecurity being put into practice. Technological solutions are provided based on an understanding between security teams and the board on what is needed, allowing for better performance in MTTR and MTTD.

Security posture needs to be fixed now

Covid-19 has heightened the risks faced by cybersecurity teams and financial services organisations, and now, more so than ever, is it vital to foster a culture of cybersecurity. The benefits of digitalisation for financial services are too great to ignore, and failure to embrace digitalisation in the name of security will hamper financial services’ growth. Instead, a holistic approach encompassing people, process and technology will be vital to forging a secure path forward in the financial services industry.

[1]https://gallery.logrhythm.com/white-papers-and-e-books/uk-the-state-of-the-security-team-research-report.pdf

[2]https://gallery.logrhythm.com/white-papers-and-e-books/uk-gain-board-level-support-for-your-security-program-e-book.pdf

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Technology

VP Bank Selects AxiomSL to Meet Multi-Jurisdictional Risk and Regulatory Reporting Requirements

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VP Bank Selects AxiomSL to Meet Multi-Jurisdictional Risk and Regulatory Reporting Requirements 2

Consolidates bank’s reporting on a single platform for financial/statistical, AnaCredit, and CRR2/Basel-driven mandates including ICAAP and ILAAP, and provides foundation for strategic expansion

AxiomSL,  the industry’s leading provider of risk and regulatory reporting solutions, today announces that VP Bank, one of the largest banks in Liechtenstein,  has selected AxiomSL’s ControllerView® data integrity and control platform, as a foundation for its risk and regulatory compliance across Liechtenstein, Luxembourg, Singapore and Switzerland, – encompassing financial and statistical reporting such as CSSF,  FINMA, AnaCredit for EBA, MAS 610 for Singapore, and CRR2- and BCBS-driven requirements including ICAAP and ILAAP for FMA.

The high-performance, fully integrated, data-driven platform will enable VP Bank to manage an array of risk and regulatory mandates on a single platform, with full transparency across all processes from ingestion, calculation, reconciliation, and validation to submission. VP Bank will use the platform strategically to further data harmonization, streamline processes, enhance automation, bolster internal controls, and strengthen risk and regulatory reporting across the enterprise.

“Selecting AxiomSL will enhance the value of our investment in regulatory technology, optimize efficiency, and deliver business insights,” stated Robert Kilga, Head of Group Financial Management & Reporting, VP Bank. “With AxiomSL’s single platform, we can ingest data in its native format from multiple sources thus creating synergies between capital, liquidity, and other business functions enterprise-wide,” he continued. “AxiomSL’s system provides intuitive, hands-on transparency into all processes from inception to filing, enhancing our confidence in the data integrity and auditability of our reporting, and enabling us to meet ever-changing regulatory requirements”.

“We are thrilled that VP Bank, such a well-respected institution, has joined our esteemed user community in the DACH region and globally,” said Claudia Thurner, EMEA General Manager, AxiomSL. “In these times of global uncertainty, complying with a wide range of regulatory and risk requirements across jurisdictions is more complex, data intensive, and time sensitive than ever. Financial institutions require a reliable technology partner who can provide global coverage while understanding the intricacies of local and regional regulatory demands,” Thurner continued. “Our industry and technical expertise will enable VP Bank to streamline their processes, scale faster, and adapt swiftly and confidently to change. We look forward to a strong and strategic collaboration with VP Bank in support of their vision and growth journey”.

With the upcoming Basel IV-driven expansion, financial institutions like VP Bank are faced with the next generation of capital requirements that can easily overwhelm systems if they lack the data transparency, proper methodologies and controls to perform calculations accurately across all risk types. These calculations may have a profound effect on the banks’ portfolio management and even the entire business model.

To address these challenges, AxiomSL’s Basel Capital Solution incorporates a flexible data dictionary architecture, seamless calculation updates, full drilldown to data and processes, transparency into model calculations, and dynamic data lineage. In addition, AxiomSL’s regulatory experts provide VP Bank with a highly efficient change-management mechanism that enables them to be current with all Basel-driven changes.

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Technology

Uncertain Times for the Financial Sector… Is Open Source the Solution?

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Uncertain Times for the Financial Sector… Is Open Source the Solution? 3

By Kris Sharma, Finance Sector Lead, Canonical

Financial services are an important part of the economy and play a wider role in providing liquidity and capital across the globe. But ongoing political uncertainty and the consequences of the COVID-19 crisis have deep implications for the UK’s financial services sector.

In a post-Brexit world, the industry is facing regulatory uncertainty at a whole different scale, with banking executives having to understand the implications of different scenarios, including no-deal. To reduce the risk of significant disruption, financial services firms require the right technology infrastructure to be agile and responsive to potential changes.

The role of open source

Historically, banks have been hesitant to adopt open source software. But over the course of the last few years, that thinking has begun to change. Organisations like the Open Bank Project and Fintech Open Source Foundation (FINOS) have come about with the aim of pioneering open source adoption by highlighting the benefits of collaboration within the sector. Recent acquisitions of open source companies by large and established corporate technology vendors signal that the technology is maturing into mainstream enterprise play. Banking leaders are adopting open innovation strategies to lower costs and reduce time-to-market for products and services.

Banks must prepare to rapidly implement changes to IT systems in order to comply with new regulations, which may be a costly task if firms are solely relying on traditional commercial applications. Changes to proprietary software and application platforms at short notice often have hidden costs for existing contractual arrangements due to complex licensing. Open source technology and platforms could play a crucial role in helping financial institutions manage the consequences of Brexit and the COVID-19 crisis for their IT and digital functions.

Open source software gives customers the ability to spin up instances far more quickly and respond to rapidly changing scenarios effectively. Container technology has brought about a step-change in virtualisation technology, providing almost equivalent levels of resource isolation as a traditional hypervisor. This in turn offers considerable opportunities to improve agility, efficiency, speed, and manageability within IT environments. In a survey conducted by 451 Research, almost a third of financial services firms see containers and container management as a priority they plan to begin using within the next year.

Containerisation also enables rapid deployment and updating of applications. Kubernetes, or K8s for short, is an open-source container-orchestration system for deploying, monitoring and managing apps and services across clouds. It was originally designed by Google and is now maintained by the Cloud Native Computing Foundation (CNCF). Kubernetes is a shining example of open source, developed by a major tech company, but now maintained by the community for all, including financial institutions, to adopt.

The data dilemma

Kris Sharma

Kris Sharma

The use cases for data and analytics in financial services are endless and offer tangible solutions to the consequences of uncertainty. Massive data assets mean that financial institutions can more accurately gauge the risk of offering a loan to a customer. Banks are already using data analytics to improve efficiency and increase productivity, and going forward, will be able to use their data to train machine learning algorithms that can automate many of their processes.

For data analytics initiatives, banks now have the option of leveraging the best of open source technologies. Databases today can deliver insights and handle any new sources of data. With models flexible enough for rich modern data, a distributed architecture built for cloud scale, and a robust ecosystem of tools, open source platforms can help banks break free from data silos and enable them to scale their innovation.

Open source databases can be deployed and integrated in the environment of choice, whether public or private cloud, on-premise or containers, based on business requirements. These database platforms can be cost effective; projects can begin as prototypes and develop quickly into production deployments. As a result of political uncertainty, financial firms will need to be much more agile. And with no vendor lock-in, they will be able to choose the provider that is best for them at any point in time, enabling this agility while avoiding expensive licensing.

As with any application running at scale, production databases and analytics applications require constant monitoring and maintenance. Engaging enterprise support for open source production databases minimises risk for business and can optimise internal efficiency.

Additionally, AI solutions have the potential to transform how banks deal with regulatory compliance issues, financial fraud and cybercrime. However, banks need to get better at using customer data for greater personalisation, enabling them to offer products and services tailored to individual consumers in real time. As yet, most financial institutions are unsure whether a post-Brexit world will focus on gaining more overseas or UK-based customers. With a data-driven approach, banks can see where the opportunities lie and how best to harness them. The opportunities are vast and, on the journey to deliver cognitive banking, financial institutions have only just scratched the surface of data analytics. But as the consequences of COVID-19 continue and Brexit uncertainty once again moves up the agenda, moving to data-first will become less of a choice and more of a necessity.

The number of data sets and the diversity of data is increasing across financial services, making data integration tasks ever more complex. The cloud offers a huge opportunity to synchronise the enterprise, breaking down operational and data silos across risk, finance, regulatory, customer support and more. Once massive data sets are combined in one place, the organisation can apply advanced analytics for integrated insights.

Uncertainty on the road ahead

Open source technology today is an agile and responsive alternative to traditional technology systems that provides financial institutions with the ability to deal with uncertainty and adapt to a range of potential outcomes.

In these unpredictable times, banking executives need to achieve agility and responsiveness while at the same time ensuring that IT systems are robust, reliable and managed effectively. And with the option to leverage the best of open source technologies, financial institutions can face whatever challenges lie ahead.

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