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BANKING ON AI TO DELIVER FRICTIONLESS CUSTOMER JOURNEYS

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Rob Mason, Chief Technology Officer, Applause

Banks and financial services companies play their cards close to their chest when it comes to IT, and Artificial Intelligence (AI) is no different. They prefer to rely on bespoke systems built internally, which can be a drawback for chatbots and smart voice assistants designed to interact with customers. The Machine Learning (ML) algorithms driving these systems need exposure to diverse data sets to prepare them for real-world interactions. Building AI systems behind closed doors might make them exclusive and secure, but without the proper training and testing, they could cause friction with the customer.

Today, most digital experiences, such as streaming or online shopping, are simple and intuitive, but digital banking is perceived to be slow and complex. Smart apps, chatbots and voice assistants are designed to streamline customer services, but all too often, they lack the training data ML algorithms need to respond to real-world situations. This can be corrected by sourcing vast amounts of data to improve an ML algorithm’s capacity to learn, successfully interact with human beings and make accurate predictions. The source of that data is people. As many are required to fulfil any number of customer use cases. In the case of a bank or a financial services company that could apply to any number of services that cater for a customer’s digital finance needs.

Rob Mason

 

Financial services companies place their trust in crowds

The pandemic has accelerated digitalization across all walks of life, the financial services space is no exception. As already mentioned, there are many areas of cloud and IT where financial services companies excel. You only have to look at the innovations in mobile and online banking. The constant stream of new digital banking products and the countless links to merchants and other service providers through API platforms that make paying for goods simple and straightforward. However, with so many banking, insurance and credit card customers now dependent on digital channels, self-service capabilities have become central to the digital banking experience. That process is reliant on the performance of AI chatbots. If they’re unable to interpret customer requests correctly or fast track them to a specific website, application or customer service agent, they can have a detrimental affect on a brand’s reputation.

Financial services companies can reduce that risk and increase levels of customer engagement through a process of rigorous crowdtesting that identifies any gaps or flaws across the different digital channels customers use. This technique involves a global community of vetted testers trialing new products before they’re released or helping to refine current applications and services. Either way, crowdtesting provides businesses with valuable insights about how customers interact with their products, improve user experiences and deliver frictionless customer journeys. It also augments in-house QA and testing resources within financial service companies and boosts the smaller, more disruptive fintech brands that tend to have limited resources.

A one-size fits all approach doesn’t really apply here. Banks and financial services companies have different tiers of customers, each with complex requirements and different expectations of what they want from digital services. Crowdtesting can be tailored to suit the needs of a specific company, by selecting vetted testers that match the profile and characteristics of their customers. They can quickly identify any bugs or processes that are causing friction and feed that data directly back to in-house QA teams. However, the impact of crowds on AI/ML is much more far reaching.

 

Data diversity is key to training ML algorithms

It takes time and effort to train and test an ML algorithm. The training process shouldn’t be restricted to a lab either. For a start, in-house teams of developers, data scientists and QA specialists tend to be from the same age range, gender and background. They’re not representative of the wider population and despite their best intentions their inherent biases will feed into the underlying algorithm. The best way to avoid this is to widen the net and ensure the training data has a high degree of a quantity, quality and diversity.

Crowdtesting allows financial services companies to source data at scale. Enabling them to select from a diverse pool of participants made up of specific demographics, including gender, race, native language, location, skill set, geography and any other filters that apply. The key is to expose the algorithm to different data sets and inputs, made up of authentic voices, documents, images and sounds. This tried and tested model ensures that the AI that doesn’t suffer from bias and has the capacity to continuously learn and improve.

For example, a recent project to train a smart voice assistant required over 100,000 different voice utterances. These utterances were delivered by 972 different people assembled remotely to train the algorithm. For another project 1,000 people contributed handwritten documents to provide the unique samples needed to ensure an algorithm could read human handwriting.

 

AIs show real intelligence

When you combine the diverse sets of training data with the community’s ability to spot any bias or other deficiencies inherent in AI-powered services during the test phase, the value of the crowdtesting model becomes clear. It can help banks and financial services companies to develop ML algorithms that can adapt to real-world conditions, reduce testing costs and release faster. By drawing on real-world experiences it’s possible to deliver exceptional levels of quality to customers across global markets, paying careful consideration to local language and culture. Gaining the well-earned trust and loyalty of those customers in return.

 

 

Banking

2022 ESG Investment Trends

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Jay Mukhey, Senior Director, ESG at Finastra

 

Environmental, Social and Governance (ESG) themes have been front and center throughout the pandemic. While the framework has been surging in popularity for several years, COVID-19 served as a period of reflection causing many companies, investors and other individuals to take these factors seriously. It’s something that we can no longer afford to ignore.

Jay Mukhey

We are witnessing drought, adverse weather patterns, hotter climates, and wildfires with more regularity, raising the profile of the climate crisis. Efforts were renewed at COP26 in Glasgow last November to help address the challenge, with the signing of the Glasgow Climate Pact and agreement of the Paris Rulebook. As a result, we are now seeing record net new inflows into ESG investing and impact.

 

Evaluating ESG criteria

Long gone are the days when ESG issues were at the periphery of a company’s operations. In just a few short years, ESG criteria have become a key metric for investors to evaluate businesses they are considering investing in.

Investor money has poured into funds that consider environmental, social and governance issues. Data from the US SIF Forum for Sustainable and Responsible Investment shows that ESG funds under management have now reached more than $16.6 trillion. It’s not just institutional investors who are embracing ESG, with Bloomberg Intelligence predicting that savers across the world will amass £30.2 trillion in ESG funds by the end of the year.

Due to the multitude of divergent factors that contribute to a company’s success on ESG, it can be tricky to pin down exactly what criteria to measure. Depending on the industry a company operates within, environmental criteria could include everything from energy usage, the disposal of waste and even the treatment of animals.

Social criteria are primarily related to how a company conducts itself in business relationships and with stakeholders. For example, does it treat suppliers fairly? Is the local community considered when the business makes decisions that would impact them? Do they have a statement and policy around modern slavery?

While governance criteria have traditionally been an afterthought, this may be changing. Everything from executive pay to shareholder rights and internal controls are relevant to investors within these criteria.

 

Tracking ESG for competitive advantage

Many experts within the financial services industry point to the power of ESG as a major competitive advantage, if used correctly. It has been noted that increasingly corporations, from big Fortune 500 companies down to small scale-ups, will communicate on their sustainability metrics to grow their business and to attract talent. However, it’s no longer enough to just pay lip service to ESG issues, with abstract commitments increasingly being seen as insufficient. Companies must now quickly progress to concrete objectives that can be measured and tracked.

A wide range of data providers now offer detailed information and tools that can measure ESG performance and effectiveness. Yet major challenges remain around bringing together what is often extremely fragmented data and transforming it into actionable insights.

 

Focus areas for 2022

The ESG criteria that investors measure is by no means stagnant. Complex societal challenges regularly emerge that require the attention of companies. Contributors recognize several topics that demand a sophisticated approach, including the COVID pandemic, diversity challenges and powerful social movements.

Companies operating within the financial services sector face several specific challenges related to ESG, with contributors believing that fintech will also continue to play a central role in finding answers to them.
For example, industry experts expect customers to be more demanding of firms in SME lending when it comes to understanding exactly what impact they are having on the climate. For many financial services firms, 2022 will be the year that they will try to reduce the time it takes to bring ESG products and services to market, such as green loans and mortgages, as well as checking accounts with sustainability and carbon tracking capabilities.

When selecting a service provider, customers are increasingly interested in the ESG credentials of their bank or financial institution. Research from PwC finds that 80% of consumers are more likely to buy from a company that stands up for environmental and governance issues. Consumers are one of the main drivers of ESG and many are putting their money where their mouth is. It’s a trend that’s not going away; financial institutions need to start implementing their strategy for ESG now.

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Opportunities for UK Challenger Banks to address AML Compliance

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Author: Gabriel Hopkins, Chief Product Officer, Ripjar

 

UK challenger banks have revolutionised the banking sector with innovative products and services,  offering greater flexibility to consumers that their legacy competitors have been unable to match. Research tells us that the value of the neo and challenger bank market will continue to grow rapidly, reaching an estimated $471 billion globally by 2027. However, the opportunities that challenger banks bring also provide new regulatory challenges, with the potential for disruptive services to  increase the risk of money laundering and other financial crimes.

Challenger Bank AML Vulnerabilities

The Financial Conduct Authority (FCA) last month raised concerns over the adequacy of challenger bank tactics in meeting regulatory requirements. The review reveals that some are falling short of effectively implementing important anti-money laundering (AML) procedures and controls, following a substantial increase in suspicious activity reports reported in 2021. The findings come as the regulator attempts to bolster its approach against money laundering, which the National Crime Agency estimates costs the UK £100bn annually.

The FCA review investigated six unnamed challenger banks that had recently entered the financial market and which together had a customer base of over 8 million customers. While the FCA commended the challenger banks’ “innovative use of technology” accelerating average customer identification and verification, it, also raised serious areas of concern stating: “there cannot be a trade-off between quick and easy account opening and robust financial crime controls.” These concerns broadly cover the following four points:

  • Failures to carry out adequate checks on customer income and occupation
  • Failures to assess customers’ risks, making it difficult to carry out due diligence measures for high risk AML alerts
  • A lack of sufficient detail in customer risk assessments
  • Unproductive management of AML alerts, hindering quick responses

The above findings as described above indicate there is a critical need for challenger banks to pair their innovative fintech capabilities with a safety-minded approach to their AML processes.

The Importance Of Customer Data

AML compliance, and the due diligence and screening processes it encompasses, may be especially complex for challenger banks since their propositions rely on swiftness, simplicity, functionality and flexibility.

The FCA’s review tells us that challenger banks’ AML issues are caused by insufficient quality of customer data with which to base precise risk-profiles and make key compliance conclusions. When challenger banks have difficulty in meeting their data collection and risk management needs, they are forced to compromise the benefits of their products and services by spending resources on AML compliance – or risking regulatory consequences.

Challenger Bank AML Solutions

Many challenger banks are able to meet their AML obligations by rolling out tailored risk management solutions, however, they may become unstuck balancing their compliance responsibilities while also delivering innovation. To keep up with the ever evolving threat landscape, the AML regulatory environment is engaged in a game of cat and mouse, often implementing new legislation to remain on the heels of new criminal tactics and methodologies.

However, rather than depending on a potentially-exposed and unproven bespoke solutions, challenger banks can instead turn to the expertise of established, industry-trusted platforms with dedicated CDD and EDD resources and multi-faceted AML and KYC screening tools.

Automated AML compliance solutions help challenger banks grapple with threats, incorporating customer data from sources across the world quickly and efficiently, and adjust in real time as the risk landscape evolves. Trusted AML solutions may include multiple language screening capabilities, helping challenger banks better manage CDD and EDD for customers around the world without producing unmanageable volumes of false positive alerts.

Financial crime is on the rise. Addressing the common weaknesses in key areas of challenger banks’ financial crime systems should be seen as a priority. Evaluating their approach to identifying and assessing the financial crime risks they are exposed to is a first step in the right direction. Extra attention should be paid to risk assessment processes to avoid running afoul of the Money Laundering Regulations. It would be especially prudent if this is carried out as the FCA has signalled they will be seeking updates from challenger banks regarding their financial crime frameworks.

 

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