Professor Jon Crowcroft, iKVA
Money laundering costs the UK economy £24bn each year, according to The National Crime Agency, whilst the UN estimates that up to $2tn is moved illegally each year via large, multinational banks. The surge in financial crime, triggered by the pandemic, has huge implications for the finance industry, as areas such as Anti-Money Laundering (AML) come under heightened pressure to prevent fraudulent activity.
The pandemic has accelerated both the adoption of digital technologies and organisational readiness, with a third of financial institutions hastening the use of Artificial Intelligence (AI) and Machine Learning (ML) in their AML strategies to fight the growing problem. Firms are thinking strategically about future proofing their technical abilities, and AI technology can help them utilise existing data to pinpoint trends and identify risks, while conserving manpower by breaking down data siloes and enabling data discovery.
Fraud, whether this is money laundering, facility takeover or application fraud, represents a real hazard for the financial sector. The analysis of substantial, siloed datasets using traditional methods of fraud detection, requires significant investment of human time and labour to scrutinise datasets and generate accurate pattern predictions.
AI technology can accelerate the examination of vast amounts of information and identify suspicious patterns and behaviours in a fraction of the time that humans can. Using large data sets, that humans would be unable to process, also enables AI to learn fraudulent data patterns and so improve detection accuracy and frequency over time.
One branch of AI, Federated Machine Learning (FML), is currently being used by the Financial Conduct Authority (FCA) to monitor fraud on a global scale. FML uses the latest AI techniques to understand the patterns of fraudulent transactions which can then be deployed internationally on decentralised edge devices hosted in individual countries. Being deployed locally enables the FCA to avoid contravening local regulations about data transfer, while benefiting from the knowledge gained from the data models. The application of AI not only removes the expense of human interaction, it increases the ability of an organisation to detect fraud, since AI typically detects patterns more accurately than humans. In 2019, the FCA used AI technology to analyse 10,000 data points across 16 regulatory returns, including their Financial Crime Data, to identify potential outliers and produce analytics to highlight risk profiles.
AI technology has a similar application in financial trading. Analysis of trading can identify patterns and combinations of patterns, that are likely to indicate suspicious activity that would indicate investment fraud. Nasdaq has introduced a deep learning system to monitor for fraud in the more than 17.5 million trades done daily on one of the world’s largest stock exchanges.
U.S. Bank is using AI technology to unlock and analyse relevant data on customers, via deep learning, and identify potential transactions that may indicate money laundering. This has doubled the output, compared with the prior systems’ traditional capabilities, clearly demonstrating the efficiency of AI software in identifying anomalous activity.
Finally, Visa’s artificial intelligence (AI)-driven security helped financial institutions prevent more than AU$354 million in fraud from impacting Australian businesses between May 2020 and April 2021. The algorithm assesses more than 500 risk attributes in roughly a millisecond to produce a score of every transaction’s predicted fraud probability. This is then used to identify patterns and alert financial institutions to potentially fraudulent activity – before it even takes place.
Accurately evaluating the financial risk of loan underwriting and credit applications, relies on multiple datapoints from a multitude of sources, which often have different search interfaces, requiring many and repeated searches to find information.
AI technology can overcome this by bringing all of these sources of information, such as bank statements, pay slips, tax filings, mortgage forms and invoices, together in one place, indexing and segmenting the data, and allowing this to be discoverable. This provides for better and more accurate results and means that decision sources can be quickly and easily identified and evaluated, helping to reduce business risk. Companies that estimated their profit from Big Data analysis have reported an average increase in revenue by 8% with a 10% reduction in costs.
Lost in translation
The finance industry generates huge volumes of data and, with a wealth of data sources worldwide, accessing filings, transcripts, research and news to discover changes and trends in financial markets in an efficient manner can be difficult – especially when the information is in different languages. For multinational corporations, operating on a global scale, time and money must be spent on translation services to understand the results.
However, Natural Language Processing (NLP) combined with Neural Networks and deep learning models, enable computers to understand the full meaning of information including sentiment and intent without the need for translation. So, advanced language agnostic AI tools, such as those developed by iKVA, enable finance operatives and regulators to discover relevant information, regardless of the source language.
The benefits that AI technology can bring to the financial industry are vast; saving time, reducing costs, improving compliance and reducing business risk. Many leading financial companies are now deploying AI on a global scale and starting to reap significant benefits, but AI is still in its infancy in the sector compared both to other industries and the potential of this technology. Appropriate utilisation of AI technologies is becoming paramount for the financial sector to reduce ever increasing fraudulent activity and to remain competitive.
How FS organisations can utilise data to boost customer experience
Charles Southwood, Regional VP and GM – Northern Europe and Africa at Denodo
We’ve all heard the age-old adage “the customer is always right”. It insinuates that, in any sector, the needs and desires of those buying a brand’s product or services should be paramount. However, today’s customer has new standards and it is becoming harder than ever for businesses to meet and exceed them.
This is certainly the case in the financial services (FS) sector where getting customer experience right used to be relatively simple. The human touch was traditionally delivered as a bi-product of in-store, transactional interactions. Perhaps, as a result of this, few people ever considered changing their provider and the traditional, established banks ruled the space.
However, with the dawn of online banking and the introduction of new, exciting challenger banks as well as the UK’s unique Current Account Switching Service, the balance of power between the consumer and the bank is changing. Consumers no longer feel locked in. If their needs aren’t being met, they aren’t afraid to look elsewhere and switch their allegiance to other companies. In other words, loyalty is far from guaranteed and customer acquisition is only half the battle.
Retention relies upon delivering strong, unique customer experiences that beat down the competition. In order to achieve this, FS organisations will need to be able to leverage data. Its insights could be the differentiator that enables them to stand out. The positive news is that, in our online world, there is a constant stream of data being produced. However, having access to all this data doesn’t necessarily mean that a brand knows how to effectively analyse and utilise it.
Ensuring data provides insight
The rapid growth in digital technologies and services across the sector has left many FS organisations juggling an unimaginable amount of data. This data is both complex and much of it is lacking in quality. Structured, semi-structured and unstructured, it is stored in many different places – whether that’s in data lakes, on premise or in multi-cloud environments. Before FS organisations can even think about using it to inform customer experience strategies, they need to be able to find it and understand it.
This is where modern technologies – such as data virtualization – can help. Through a single, logical view data virtualization boosts visibility and real-time availability of all data across an organisation. Unlike traditional extract, transform and load (ETL) solutions, it does not move and copy data. Instead it leaves it in the source systems. In other words, instead of just replicating data, data virtualization reveals an integrated view to those trying to find it.
For FS organisations this provides several important benefits. For example, it helps when data sovereignty issues arise and the movement and replication of data outside certain countries is illegal. Data virtualization solutions can also assist in terms of financial reporting by fetching data in real time from underlying source systems – applying the necessary security and obfuscation whilst delivering the performance, the agility and the accuracy needed through the seamless connection of data.
FS organisations that adopt data virtualization, are likely to see an improvement in the overall performance and efficiencies of their business operations. Overheads will be reduced, as will the length of project times. Above all, data virtualization will rapidly strengthen the customer experience by supporting business leaders to think strategically and make decisions based on real-time insights. But don’t just take my word for it.
The proof is in the pudding: How Landsbankinn is delivering on the CX promise
Landsbankinn is just one of the many financial services institutions that has already successfully embraced data virtualization and its benefits. Despite being the largest financial institution in Iceland – with around 40% of the retail and 33% of the corporate banking market share – Landsbankinn used to face several issues when it came to data sharing and analytics.
Over 45 siloed data sources – including Oracle databases, data warehouses and APIs from internal and external sources – made finding and accessing the right data at the right time extremely difficult. Without real-time data to fuel informed decision making, customer experience and operational efficiency were suffering. As a result, Landsbankinn was in need of a data overhaul to streamline and integrate its infrastructure.
To bring together its complex data landscape and collect data in real-time, Landsbankinn implemented the Denodo Platform – a data integration and data management solution built on data virtualization – to build a logical data warehouse. As a result, the team can now aggregate data from multiple data sources, transform that data based on the applied business rules, and then make it available to consuming applications. Ultimately, this means that, throughout the organisation, the data can be utilised by a wealth of employees, even those who are not particularly IT savvy. It also means that the business leaders can use data insights to make well-versed decisions and provide a plethora of services to Landsbankinn customers both quickly and efficiently.
In recent years, customer retention has become the key to successfully growing a business. This cannot happen without an effective customer experience strategy. The ability to convert data into insight is priceless in an economic landscape where the line between a business thriving, surviving and failing is so thin. Those operating in financial services must harness modern technologies – like data virtualization – to stay at the top of their game and ahead of the competition.
The Evolution of SoftPoS in 2023
By Brad Hyett, CEO of phos
Contactless payments and digital wallets have surged in popularity in recent years. Part of this stems from the digital boom that occurred during COVID-19 but it’s also thanks to the ease of use that contactless offers customers. This has helped accelerate Software Point of Sale or ‘SoftPoS’ adoption amongst SMEs and enterprise retailers, with a total of 6 million merchants taking advantage of the technology in 2022 according to Juniper Research.
SoftPoS or ‘Tap to Pay’ technology – is a software solution that allows vendors to turn their phones or mobile devices into contactless payment points. This has made life for small businesses easier, as they no longer have to fork out large sums of money for traditional Point of Sale (POS) terminals, i.e. card readers, or ‘make do’ with outdated payment software.
In light of Apple’s announcement to allow third-party SoftPoS providers to deploy their technology on iPhone last year, adoption is expected to increase further. By 2027, it’s forecast that there will be up to 34.5 million merchants by 2027 – nearly a 500% increase from today. With more payment giants like Paypal and Venmo announcing they will support contactless transactions through their iOS apps in the months ahead, what else is in store for SoftPoS in 2023?
Apple’s role in market consolidation within SoftPoS
Apple’s move to integrate the technology with iOS devices will expand SoftPoS’ usability across mobile operating systems – significantly boosting the size of the addressable market for vendors. For the first time, Apple users will be able to offer Tap-to-Pay solutions which have traditionally been limited to Android devices only.
This will ultimately bring greater awareness and adoption of SoftPoS as we see increased familiarity with Tap-to-Pay solutions among businesses and consumers alike – as they’re no longer bound by the constraints of the type of phone they use.
While the SoftPoS on iPhone rollout currently only applies to the US market, it’s fair to assume this will expand internationally at some point – aiding the normalisation of ‘Tap to Pay’ solutions en masse in the months and years ahead.
The next wave of solopreneurs
The events of the last year will also continue to have a ripple effect over the next 12 months. For example, we’ve seen the tech industry undergo mass layoffs due to a challenging economic environment and rising global inflation.
With large numbers of highly skilled talent out of work, the phenomenon of solo entrepreneurship is likely to see an uplift – as it did during the pandemic – over the next 12 months. Born in a digital-native environment, individuals from this released workforce can now set up their own businesses and run them on mobile devices, as opposed to legacy infrastructures.
This could prove another sizable opportunity for SoftPoS vendors in the coming year, as we predict to see more small businesses sprout as a result of ongoing redundancies.
The growing importance of SoftPoS orchestration
As the market rapidly develops, so too does the choice and ease of onboarding. Financial institutions and retail technology providers can now use a SoftPoS orchestrator to help them deploy Tap-to-Pay solutions quickly and easily for their merchant customers, instead of having to create their own mobile solutions. This saves them time and money – both crucial resources for any business and especially in a challenging economy.
Partnering with a SoftPoS orchestrator is a cost-effective way of providing mobile payment solutions without having to worry about waiting on new software and security updates. With an orchestrator, this is done automatically – making this a much lighter lift with no requirement for technological know-how.
As SoftPos orchestrators are acquirer agnostic, this means they can help businesses provide a SoftPos solution to their own retail customers, regardless of the existing acquirer that they’re already using.
An additional benefit here is that a wider pool of merchants are able to benefit from the technology – growing the overall size of the SoftPoS market. Orchestrators, then, have the ability to drive wider adoption of the technology globally, reaching a bigger audience of end users and advancing the mobile payments industry in emerging markets across the world.
The increased popularity of digital and contactless payment options has driven exponential growth in the SoftPoS market in recent years. The next 12 months will see the technology enter the mainstream, as Apple starts to allow more third-party SoftPoS providers to deploy their solutions on iPhones.
The timing coincides with several emerging opportunities for the technology, including a potential uptick in the number of solopreneurs and mobile-first businesses. This combination of factors will see more financial institutions and legacy technology players work with SoftPoS orchestrators to bring Tap-to-Pay solutions to market in 2023 if they want to stay ahead of the competition and keep up with ever evolving customer demands.
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