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IMPLEMENTING MACHINE LEARNING: FIVE LESSONS FROM FINANCIAL SERVICES ORGANISATIONS

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IMPLEMENTING MACHINE LEARNING: FIVE LESSONS FROM FINANCIAL SERVICES ORGANISATIONS

By Steve Wilcockson, Industry Manager – Financial Services, Mathworks.

Steve Wilcockson

Steve Wilcockson

The financial services industry has long been a hub for technological innovation. So it’s not surprising that banks, asset managers, insurers and even supervisors are exploring how they can apply machine learning.

Interest in the technology has grown rapidly over the last few years.  For example, back in 2014, we surveyed financial professionals and found only 12% were using machine learning in their workflows. When we repeated the study in 2016 with risk and quant analysts attending our London computational finance event, the number using machine learning had almost quadrupled to over 40%.  What’s more, half of those who said they weren’t doing anything today said they planned to start projects within 12 months.

This appetite to embrace machine learning is driven by a strong belief that the future of the industry will be decided by financial computing engineers, and their algorithms. But, stripping away the headlines and hyperbole, what’s the current state of play?

Today, the application of machine learning is progressing at different rates across the industry segments. Buy-side players, competing to increase returns and take (sensible) risks,are increasingly applying machine learning techniques as an extension of their factor analysis suites to differentiate from peers and rivals. One asset manager we know well uses machine learning to determine correlation and predictive trends across macroeconomic, credit, liquidity, risk and money flow factors. This allows them to better understand asset class performance trends, with some of their portfolios outperforming benchmarks by 100 basis points.

The sell-side has a more nuanced “risk management-first” view, oriented around task, skills and, importantly, regulation. Data science and big data teams, sometimes born from Quant departments and other times IT, often focus on “test” problems, such as fraud detection, money laundering investigation, or modelling customer activity. However, methodologies in some cases are impacting mainstream functions, for example as adaptable means of modelling credit risk dynamics. Regulators can be sensitive to the use of such approaches because in some instances machine learning approaches are considered “black box”, but where they are well-documented and discussed, they are increasingly accepted.

The challenges of bringing machine learning into a complex financial business remain daunting. This can be due to the quantity, type and variety of data being analysed. Other challenges might include determining the best model approach for the problem at hand, and ensuring participants are comfortable with its role in the process.

So, as the industry evolves its use of machine learning, what lessons can we learn to generate success?

  1. Understand your mandate

Like any complex project, set clear goals about what you want to achieve, and the role of machine learning in achieving them. If considering developing “intelligent” robo-advisers, for example, how intelligent do they need to be, when and how will difficult questions be passed to a human, what human roles will be replaced or augmented, and how will your customers respond to robo-advice? Most importantly, what will the human/automation interface look like? The head of one Wall Street hedge fund pushing ahead with greater use of machine learning summed this up well as “No man is better than a machine. And no machine is better than a man with a machine.”

  1. Pick low hanging fruit but be strategic

Running machine learning in a standalone greenfield project can be a sensible first step, but it may be hard to demonstrate sufficient gains and pitfalls. Incorporating machine learning methods into existing processes can be more demonstrable. Consider running shadow projects to compare and contrast performance with existing ones, for example to assess whether retail credit requests such as mortgage extension requests may reflect default conditions or are reasonable requests for (reasonable) asset financing. This allows relative quantifiable benchmarking. Consider multiple measures too. Accuracy is one thing, but model lifecycle might be another. One advantage to both bank and regulator for example in the adoption of so-called bagged decision trees as a means of modelling credit ratings adjustment might result in a 4x to 10x model lifetime compared with a less adaptable traditional approach. This can save development and maintenance time and effort. As one machine learning expert at a major international bank recommends, tackle low hanging fruit to achieve 80 percent of the impact in five percent of the time.

  1. Together, Stronger

Machine learning might appear to be a subject for a small band of technologists and thus the preserve of the mathematician and computational elite, but recognize that others bring relevant skills.

Cross functional teams matter hugely. Software engineers bring the ability to speed up model creation and implementation gains; project managers bring project lifecycle management; model users and domain experts bring the ability to state design requirements and suggestions that lead to differentiating and useful insights. To ensure all this activity doesn’t fly apart, communication, coordination and support across the functions matter,keeping the team tight and successful.

Yes, your machine learning expert may be your team’s star player but it takes a strong performance from the whole team to ensure successful execution.  You need to prepare to manage, and overcome failure.  Similarly, it’s important to continue to learn, adapt and improve, both as individuals and as a team. Your team needs to consolidate around its own slick cross functional working environment, developing models and implementing them rapidly.

Your team also must acquire new blood, to develop, apply, implement and interpret new methodologies better, to fill gaps, acquire code and expertise from beyond your organizational boundaries. Kaggle, who run hundreds of data science competitions, can provide competitive leader boards for driving energetic internal problem solving and tapping hidden talents for a machine learning project. It’s quite possible to incorporate these contributions into your own projects.

  1. Make it Human

Ultimately user engagement is key. Machine learning is useless without meaningful output. Machines may know best in many cases, but value comes from human interpretation and use. Humans are visual creatures, so consider “simple” visualization as much as you do the most complex model. Avoid misleading graphical representation and do consider subtle nuances that can communicate insights most forcefully. As one big data lead in a global bank stated,“a good font in a visualization is like being more mentally engaged when hearing a nice accent”. Such good aesthetics means key stakeholders can make a faster mental connection with what they’re being shown when sometimes fully and quickly understanding the most complex model output can be challenging.

  1. No one size fits all

Every task is different, so use platforms and tools that you can immerse yourself in to understand those differences, to import whatever data-set you need, to try out multiple algorithms quickly and easily perhaps as part of near-at-hand applications. Cross-validate and test, parallelize to speed-up model execution in clouds and on GPUs, and get trustworthy solutions out to the people who need them, as web applications, in spreadsheets and as database components.

But know your risk. Bad tools add risk as much as good tools can facilitate faster time-to-market and enhance productivity. Know which tools you are using, how trustworthy the routines are, and how comprehensive the supporting help and documentation is.  Consider how efficient their algorithms are, and if faced with a difficult time-consuming task, how easily scaled is it in a distributed set-up. Software for developing and implementing machine learning and data science can be like the Texas rangers in the days of the Wild West, at worst toxic, dangerous and unpredictable and at best bring order to the frontier. As far as possible, seek your own order amidst chaos, yet also take care not to lose sight of the opportunity and the excitement of the unknown.

The financial services industry’s use of technology can be contradictory; fast-paced yet also conservative. Nonetheless, the sector is increasingly leveraging machine learning as part of a broad tool-set to address challenging problems, and build exciting new use cases.

Finance

How payments can help streamline operations and boost customer satisfaction in the vending industry

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How payments can help streamline operations and boost customer satisfaction in the vending industry 1

By Darren Anderson, Business Development Manager, Self Service, Ingenico Enterprise Retail

The COVID-19 pandemic has had an astounding impact on the payments industry, causing cash usage to plummet as contactless and card-not-present volumes soared. Of course, this phenomenon was not unforeseen by payments professionals, who had predicted such a movement away from cash, but not at the speed the virus guidelines facilitated. In fact, due in part to the hygiene perks of contactless payment methods increasing its adoption, 50% of customers think that cash will disappear completely at some point in the future.

The unattended market was ahead of the pandemic in terms of contactless alternative payment method (APM) adoption, and it continues to upgrade its offerings to suit a wider range of industries. Nevertheless, the pain point for vending operators is that they’re often not sure exactly how these technologies work, or how to implement them. And with payments offerings constantly evolving, it’s becoming harder for vending operators to know which solution would be the best fit for their business.

As such, one easy way for vending operators to ease this load is to partner with a knowledgeable payments advisor who can not only provide the best solutions for their business, but guide them through the process and any need-to-knows. It’s also important to investigate the payments trends across the vending market, what the future might bring and what vending operators need to know about newer payments technology and the value it can bring to their unattended retail business operations.

Vending through the pandemic

Coronavirus has impacted the unattended market in various ways. In some cases, vending machine use has decreased as a result of lower footfall and closed premises. However, the nature of vending being self-service, for many it’s just been a case of upgrading systems to meet new guidelines and hygiene recommendations to start boosting their usage again. As cash usage decreased over the course of the pandemic, cards and APMs stepped in to provide a host of benefits, and as customers use and enjoy these seamless technologies, they are fast becoming the preference.

These developments have provided the opportunity for vending operators to embrace newer technologies which, although ultimately positive, can prove daunting if such retailers are not accustomed to working closely with payments. Fortunately, the vending market is in a great position to take advantage of new contactless technologies, being already low on human interaction and having 24/7 capabilities.

Darren Anderson

Darren Anderson

What’s more, the market can not only cater to consumers’ evolving needs, but it can also provide the flexibility and reliability that consumers are relying on as the world around them is changing. Many new technologies can also improve the general operations and management of vending, offering features such as easier on-the-go stock management and maintenance notification technology.

Keeping the consumer in mind

Consumers today want to enjoy the latest innovations and best-in-class customer experiences. These shoppers believe that self-service is a time-saver, and they also view cashless and contactless as faster and more seamless ways to pay – a fact which is reflected in the recent consumer demand for a wider variety of APMs. Customers now expect even more options to pay for their goods and services, from QR codes, to in-app payments and more.

Alongside the cashless trend, data-security and customer experience are two other factors driving the vending market evolution. With constantly evolving fraud developments in the online world, good security is more pertinent than ever, and has to be a central consideration to vending operators – as well as ensuring a seamless customer experience.

From a customer usage standpoint, mobile payments are becomingly increasing popular, as driven by the Gen Z market. According to our research, 63% of Gen Zers have said they would pay more for a mobile experience[1].

Trust and a good experience are also considerable factors across all customer groups, with 95% of customers claiming their loyalties lie with a company they trust[2], and 86% willing to pay more for a positive experience[3].

To appeal to ever-hungry consumers, vending operators need to provide the options they want. In the unattended market, this is relatively simple – not only do they provide a convenient and reliable method of payment for customers, but they also avoid face-to-face interaction. They can also supply a range of different products and accept a variety of payment methods to appeal to all customers, no matter their preference.

Using payments to drive revenue

Driving revenue is a two-pronged approach – you need to appeal to customers to keep them coming, and streamline operations to reduce overheads. In order to meet both parties’ expectations, it’s important to respond well to new vending challenges, taking note of the solutions that enable merchants to provide their customers with the payment methods they prefer.

Payments are complicated, so there’s no need to worry if you’re not hugely familiar with the offering out there, or unsure where to start – that’s where a payment service provider (PSP) can assist. With the expertise that a PSP brings, along with the technological solutions they offer, vending operators can improve customer journeys in all unattended environments.

Such technological solutions are flexible and can cater to specific business needs, while providing easy, quick, and secure payment methods that protect both the business and the customer’s personal data. They can also improve operational efficiency, increasing business performance with features such as real-time reporting and smart transaction management, to provide a best-in-class customer experience.

With smart devices, a secure gateway and advanced acquiring capabilities, PSPs can help vending operators design a flexible vending solution tailored to their individual and specific needs. To find out more about unattended retail and how your company can benefit from Ingenico’s unique expert knowledge, get in contact with Ingenico Enterprise Retail today at www.ingenico.com/smartselfvending.

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ISO 20022 migration: full speed ahead despite recent delays, says new Deutsche Bank paper

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ISO 20022 migration: full speed ahead despite recent delays, says new Deutsche Bank paper 2

Today, Deutsche Bank has released the third installment in its “Guide to ISO 20022 migration series, which offers a comprehensive update on the industry shift to the de facto global standard for financial messaging: ISO 20022. This paper comes at a critical time for the ISO 20022 migration, with a number of changes to existing timelines and strategies from SWIFT and the world’s major market infrastructures having been announced this year.

The paper explores the latest developments, including SWIFT’s year-long postponement of the migration in the correspondent banking space. The decision meets industry calls for a delay and also provides ample time to build the new central Transaction Management Platform (TMP) – a core feature of SWIFT’s new strategy that will allow the industry to move away from point-to-point messaging and towards central transaction processing.

It also details the wave of action that has been seen by market infrastructures around the world – with many, including the ECB, EBA CLEARING and the Bank of England, announcing revised migration approaches.

“Now more than ever, with shifting timelines and strained resources, it is vital that banks and corporates alike do not view the ISO 20022 migration as just another project that can be put on the back burner,” says Christian Westerhaus, Head of Cash Products, Cash Management, Deutsche Bank. “The delays in the correspondent banking space, and across several market infrastructures, should not be seen as an opportunity for banks to take their foot off the pedal. The journey to ISO 20022 is still moving ahead at speed – and internal projects need to reflect this.”

The Guide also highlights the implementation issues on the migration journey ahead – most notably surrounding interoperability between market infrastructures, usage guidelines and messaging formats. This is achieved through a series of deep dives, case studies, and points of attention drawn from Deutsche Bank’s internal analysis.

 “As this year has proved, nothing is set in stone, “says Paula Roels, Head of Market Infrastructure & Industry Initiatives, Deutsche Bank. “The ISO 20022 migration involves a lot of moving parts and keeping abreast of the latest developments is critical for banks and corporates alike. As the deadlines near, and the ISO 20022 story develops, this series of guides will continue to highlight key points for consideration over the coming years.”

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The Psychology Behind a Strong Security Culture in the Financial Sector

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The Psychology Behind a Strong Security Culture in the Financial Sector 3

By Javvad Malik, Security Awareness Advocate at KnowBe4

Banks and financial industries are quite literally where the money is, positioning them as prominent targets for cybercriminals worldwide. Unfortunately, regardless of investments made in the latest technologies, the Achilles heel of these institutions is their employees. Often times, a human blunder is found to be a contributing factor of a security breach, if not the direct source. Indeed, in the 2020 Verizon Data Breach Investigations Report, miscellaneous errors were found vying closely with web application attacks for the top cause of breaches affecting the financial and insurance sector. A secretary may forward an email to the wrong recipient or a system administrator may misconfigure firewall settings. Perhaps, a user clicks on a malicious link. Whatever the case, the outcome is equally dire.

Having grown acutely aware of the role that people play in cybersecurity, business leaders are scrambling to establish a strong security culture within their own organisations. In fact, for many leaders across the globe, realising a strong security culture is of increasing importance, not solely for fear of a breach, but as fundamental to the overall success of their organisations – be it to create customer trust or enhance brand value. Yet, the term lacks a universal definition, and its interpretation varies depending on the individual. In one survey of 1,161 IT decision makers, 758 unique definitions were offered, falling into five distinct categories. While all important, these categories taken apart only feature one aspect of the wider notion of security culture.

With an incomplete understanding of the term, many organisations find themselves inadvertently overconfident in their actual capabilities to fend off cyberthreats. This speaks to the importance of building a single, clear and common definition from which organisations can learn from one another, benchmark their standing and construct a comprehensive security programme.

Defining Security Culture: The Seven Dimensions

In an effort to measure security culture through an objective, scientific method, the term can be broken down into seven key dimensions:

  • Attitudes: Formed over time and through experiences, attitudes are learned opinions reflecting the preferences an individual has in favour or against security protocols and issues.
  • Behaviours: The physical actions and decisions that employees make which impact the security of an organisation.
  • Cognition: The understanding, knowledge and awareness of security threats and issues.
  • Communication: Channels adopted to share relevant security-related information in a timely manner, while encouraging and supporting employees as they tackle security issues.
  • Compliance: Written security policies and the extent that employees adhere to them.
  • Norms: Unwritten rules of conduct in an organisation.
  • Responsibilities: The extent to which employees recognise their role in sustaining or endangering their company’s security.

All of these dimensions are inextricably interlinked; should one falter so too would the others.

The Bearing of Banks and Financial Institutions

Collecting data from over 120,000 employees in 1,107 organisations across 24 countries, KnowBe4’s ‘Security Culture Report 2020’ found that the banking and financial sectors were among the best performers on the security culture front, with a score of 76 out of a 100. This comes as no surprise seeing as they manage highly confidential data and have thus adopted a long tradition of risk management as well as extensive regulatory oversight.

Indeed, the security culture posture is reflected in the sector’s well-oiled communication channels. As cyberthreats constantly and rapidly evolve, it is crucial that effective communication processes are implemented. This allows employees to receive accurate and relevant information with ease; having an impact on the organisation’s ability to prevent as well as respond to a security breach. In IBM’s 2020 Cost of a Data Breach study, the average reported response time to detect a data breach is 207 days with an additional 73 days to resolve the situation. This is in comparison to the financial industry’s 177 and 56 days.

Moreover, with better communication follows better attitude – both banking and financial services scored 80 and 79 in this department, respectively. Good communication is integral to facilitating collaboration between departments and offering a reminder that security is not achieved solely within the IT department; rather, it is a team effort. It is also a means of boosting morale and inspiring greater employee engagement. As earlier mentioned, attitudes are evaluations, or learned opinions. Therefore, by keeping employees informed as well as motivated, they are more likely to view security best practices favourably, adopting them voluntarily.

Predictably, the industry ticks the box on compliance as well. The hefty fines issued by the Information Commissioner’s Office (ICO) in the past year alone, including Capital One’s $80 million penalty, probably play a part in keeping financial institutions on their toes.

Nevertheless, there continues to be room for improvement. As it stands, the overall score of 76 is within the ‘moderate’ classification, falling a long way short of the desired 90-100 range. So, what needs fixing?

Towards Achieving Excellence

There is often the misconception that banks and financial institutions are well-versed in security-related information due to their extensive exposure to the cyber domain. However, as the cognition score demonstrates, this is not the case – dawdling in the low 70s. This illustrates an urgent need for improved security awareness programmes within the sector. More importantly, employees should be trained to understand how this knowledge is applied. This can be achieved through practical exercises such as simulated phishing, for example. In addition, training should be tailored to the learning styles as well as the needs of each individual. In other words, a bank clerk would need a completely different curriculum to IT staff working on the backend of servers.

By building on cognition, financial institutions can instigate a sense of responsibility among employees as they begin to recognise the impact that their behaviour might have on the company. In cybersecurity, success is achieved when breaches are avoided. In a way, this negative result removes the incentive that typically keeps employees engaged with an outcome. Training methods need to take this into consideration.

Then there are norms and behaviours, found to have strong correlations with one another. Norms are the compass from which individuals refer to when making decisions and negotiating everyday activities. The key is recognising that norms have two facets, one social and the other personal. The former is informed by social interactions, while the latter is grounded in the individual’s values. For instance, an accountant may connect to the VPN when working outside of the office to avoid disciplinary measures, as opposed to believing it is the right thing to do. Organisations should aim to internalise norms to generate consistent adherence to best practices irrespective of any immediate external pressures. When these norms improve, behavioural changes will reform in tandem.

Building a robust security culture is no easy task. However, the unrelenting efforts of cybercriminals to infiltrate our systems obliges us to press on. While financial institutions are leading the way for other industries, much still needs to be done. Fortunately, every step counts -every improvement made in one dimension has a domino effect in others.

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