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Compliance and the cloud – a match made in heaven?

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Compliance and the cloud - a match made in heaven?

Ed Addario, Chief Technology Officer at Currencycloud

Move fast and break things. That’s been the mantra when it comes to innovation for over a decade, championed by the likes of Facebook, Google and Amazon, who tore up the rulebook to establish innovative services that dominate across the world.

But for sectors such as banking, there are rules and regulations to follow when it comes to being compliant. And this can come at a price to innovation. According to our Future payments research, almost a third of businesses (28%) think regulation and compliance hamper innovation.

However, innovation and compliance, in my opinion, shouldn’t be an either/or decision. In fact, innovation, particularly in the banking sector, can be fueled by compliance and regulation – helping businesses work better internally and with partners and suppliers, particularly when it comes to the cloud. Let me explain. 

Regulation, regulation, regulation

Know Your Customer (KYC), Anti-money Laundering (AML), Payment Services Directive 2 (PSD2) and the General Data Protection Regulation (GDPR) represent the recent raft of regulation that financial services providers must contend with. These vary from nation to nation and sometimes even within the same country.

The cost of making compliance errors – failing to check all relevant documents, lack of secure storage, incorrect permissions for data access – is significant. There are the standard fines, of course. But beyond this is a core issue of trust. Poor use of customer information costs you customers – it’s as simple as that.

But not only do businesses have to navigate compliance when developing new ideas, they also somehow have to mitigate the effects that compliance has on their customer experience. Going through the KYC process as an end-user of financial services is a bore. So many pieces of paper evidence, many of them originals and with expiration dates. So much time waiting for them to be processed. And, on occasion, having to repeat the process because someone’s finger slipped during the data entry stage. This presents a tangible hit to providers’ bottom lines.

Regulation to fuel innovation

However, while not often familiar bedfellows, regulation in banking is driving efficiency and innovation. The recent release of two seemingly contradictory regulations – PSD2, which opens up notoriously data sensitive bank accounts to third party access, and GDPR, which cracks down on data use without explicit permission – is actually creating an environment where the digitisation, and adoption of cloud-based technology, of KYC, AML and other processes is being encouraged rather than challenged.

Increased regulation means companies working with data will have to shore up their own lax procedures, assessing data held in public cloud environments or stored on unsecured personal devices. But the regulation also pushes the agenda for increased digitisation of data, the need to hold it securely in centralised sources while improving access for legitimate users. So much so that governments around the globe are joining the push for electronic identity documents – driving the need for innovative new ways to adapt to regulation.

As with mobile payments, the inspiration for innovation has come from developing countries. In 2008 the Indian government created the Unique Identification Authority of India (UIDAI) to give a unique digital identity to every resident. Targeting the whole population, but in particular the large numbers of undocumented individuals, the electronic ID called Aadhaar gave people access to healthcare, services and banking.

A GSMA study found the consequence has been to speed up e-KYC, primarily in the mobile sector but with implications for MNOs to become payments banks for low-income customers. In doing so, it reduced the cost of KYC processes from $0.60 per customer to $0.07.

Cloud-based KYC is the way forward, it relies on centralised, protected data sources that are expertly maintained to ensure that they remain current and compliant. Centralisation provides the access needed to make cross-border payments a reality. From finding common integrations across different national banking systems to validating customer IDs in more than one territory, Cloud-based KYC is breaking down barriers both real and virtual.

Elsewhere, the notion that regulation supports innovation is echoed in the way the Competition and Markets Authority (CMA) in the UK is actively supporting PSD2 as a way to open up competitiveness in the banking sector. This in turn is driving better customer experience, as well as streamlined business practices. Regulation is being used as a catalyst for many organisations to ‘clean house’ in a way that many have put off for reasons of ‘tradition’ or ‘legacy’. 

Moving towards a world of digital-first financial products is both inevitable from a customer satisfaction point of view and desirable for institutions. What’s required is an innovative mindset that’s willing to use the various rules and regulations as a source of information, rather than an inconvenience, adapting to the regulations to ensure that you can move as agile and as fast as possible.

To read more about how financial businesses can access these new, agile services, read the future of payments report here.

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NICE Unveils ENLIGHTEN Fraud Prevention Powered by AI and Voice Biometrics to Empower Contact Centers in Safeguarding Consumers

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NICE Unveils ENLIGHTEN Fraud Prevention Powered by AI and Voice Biometrics to Empower Contact Centers in Safeguarding Consumers 1

Using AI-enabled interpretive and predictive models and advanced voice biometrics, the new solution continuously scans millions of calls to proactively identify fraudulent behavior and protect brand reputation

NICE (Nasdaq: NICE) today unveiled ENLIGHTEN Fraud Prevention, an innovative new solution for automatic and continuous fraudster detection and exposure. Bringing together NICE ENLIGHTEN’s comprehensive Customer Engagement AI platform with the company’s voice biometrics capabilities, the solution continuously scans millions of calls to accurately pinpoint suspicious behavior and uncover previously unidentified fraudsters. Adopting a proactive approach, NICE ENLIGHTEN Fraud Prevention significantly reduces fraud losses and handling time while protecting consumers and improving their experience.

“Contact center fraud is growing in frequency, breadth and sophistication,” observes Dan Miller, Lead Analyst at Opus Research. “NICE ENLIGHTEN Fraud Prevention stands out as an integrated, pre-emptive AI-based Fraud Prevention solution that actively prevents malicious activities with minimum additional effort from customers.”

Unlike most technologies that focus on a single call, NICE ENLIGHTEN Fraud Prevention includes powerful AI interpretive and predictive models that scan millions of voice interactions over time to detect abnormal, risky behavior including requests to change addresses or authentication methods without relying on agents to manually capture dispositions. NICE’s Proactive Fraudster Exposure voice biometrics capability included within the solution is then used to expose perpetrators and create a ranked and prioritized list of suspected fraudsters. Importantly, the solution is self-training, constantly learning from identified behaviors, continuously updating its AI models and thus consistently improving results. With this novel solution, organizations can protect customers from account takeover and prevent exposure of personally identifiable information, reduce fraud losses, optimize fraud analyst team efficiency and safeguard brand loyalty.

“We are proud to bring yet another market-first offering with NICE ENLIGHTEN Fraud Prevention,” Barry Cooper, President, NICE Enterprise Group, said. “NICE ENLIGHTEN is NICE’s AI platform with models specific to the Customer Engagement domain. A number of solutions across our portfolio are being infused with AI from NICE ENLIGHTEN including our Proactive Fraudster Exposure solution. NICE ENLIGHTEN Fraud Prevention ensures that fraudsters are rapidly and proactively stopped in their tracks so organizations can protect their customers and their brand. We believe that by bringing AI to Fraud Prevention we provide organizations with the agility that makes it even more difficult for the fraudsters to win.”

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Financial Services Sector Leads in Fixing Application Flaws, Lags in Time to Remediate

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Financial Services Sector Leads in Fixing Application Flaws, Lags in Time to Remediate 2

Veracode, the largest global provider of application security testing (AST) solutions, today released findings revealing that the financial services industry has the best flaw fix rate across six industries and leads a majority of industries in uncovering flaws within open source components. Fixing open source flaws is critical because the attack surface of applications is much larger than developers expect when open source libraries are included indirectly.

The findings came as a result of Veracode’s State of Software Security Volume 11, which analysed 130,000 applications from 2,500 companies. The research found that financial services organizations have the smallest proportion of applications with flaws and the second-lowest prevalence of severe flaws behind the manufacturing sector. It also has the highest fix rate among all industries, fixing 75% of flaws. Still, the research found that financial services firms require about six and a half months to resolve half of the flaws they find, indicating it is slower than other industries to remediate.

“Financial services firms have a median time to remediation of more than six months, despite having a high fix rate compared to other sectors,” said Chris Wysopal, Chief Technology Officer at Veracode. “However, developers in the financial services industry are often limited by the nature of the environments they are working in, as applications tend to be older, have a medium flaw density, and aren’t consistently following DevSecOps practices compared to other industries. With some additional training and sticking to best practices, they can quickly remediate issues and start to reduce security debt.”

Financial Services Specific Findings

Veracode’s research found compelling evidence that certain developer behaviours associated with DevSecOps yield substantial benefits to software security. The findings detail that financial services firms:

  • Are a leading industry when it comes to fixing flaws in their open source software and establishing strong scan cadences.
  • Fall to middle-of-the-road for scanning frequency and integrating security testing, and are not likely to be using dynamic analysis (DAST) scanning technology to uncover vulnerabilities.
  • Outperform averages across all industries in dealing with issues related to cryptography, input validation, Cross-Site Scripting, and credentials management – all things related to protecting users of financial applications.
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AI: Do the Right Thing

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AI: Do the Right Thing 3

By Alix Melchy, Jumio VP of AI

The application of emerging technologies such as AI, cloud, blockchain and IoT in financial services has altered the traditional operating models of financial institutions, the competitive dynamics of the industry, the role of people in those institutions and the landscape of the financial system as a whole. In fact, AI is positioned as an essential investment, with the World Economic Forum arguing how it is set to become central to the fabric of financial institutions.

While the adoption of AI in financial services may be in its infancy, the use cases are ever growing. From recommending loan and credit offerings to detecting fraud, 94% of financial services in European and Middle Eastern markets believe that AI will disrupt their business. The direction and the awareness of AI is clear but it is essential that companies invest now, as if done too hastily, the process is marred by pitfalls.

Despite the transformative promise of AI and machine learning algorithms, we have seen its application come under scrutiny in other industries. Take the UK A-Level exam grading debacle that dominated headlines back in August. Exam grades of students living in certain UK postcodes were disproportionately and negatively impacted, while other students saw their results inflated. This was down to an algorithm implemented by Ofqual that was set to predict grades using historical data including grades obtained at exams in previous years.

The incident raises the question as to what would happen if the algorithm used in this instance was applied to a financial decision. The same biases could negatively impact the way millions of consumers and businesses borrow, save and manage their money.

It is therefore imperative that financial institutions learn from this scenario, ensuring that when implemented in financial decision-making, AI is nothing short of a success.

AI is no fairy godmother

While many tout the game-changing effects of the looming AI revolution, it’s fundamentally important to understand that AI is not magic. Instead, we need to learn to set reasonable expectations with AI so not to paint an unrealistic picture of its power.

In order to start out on the right track, businesses must first define and align on the task they want the algorithm to perform before it can be developed and implemented. Articulating the problem to be solved is the prerequisite for a solid framework of development and evaluation of your algorithms.

Removing bias in AI

AI is the tool, not the hand that wields it or the eye that guides it. It is a type of learning system that requires data, training integration, and course correction. Just as we would train a young engineer to use a tool correctly, we are training AI systems to become expert learning systems through the data, process and people.

Therefore, in order to solve a problem using AI, the task must be expressed in a form which a machine can understand and the machine must be supplied with the necessary data to perform or otherwise learn to generate predictions that enable it to accomplish its objective. Without strong and relevant data underpinning an AI model, it will never be able to produce strong and relevant results.

To design a fair algorithm, the key is to collect a sufficient amount of data so that the algorithm can be trained to represent an entire community. While it is possible to buy datasets to speed up the process, when doing so, it is essential that the data meets your required criteria rather than simply being a large data set. For the financial services sector, this enables employees to treat customers fairly and, when combined with appropriate modelling and processes, allows them to maintain transparency and accountability in their decision-making processes to avoid legal claims or fines from regulators which can cause deep reputational damage.

Building back better

As the Ofqual issue revealed, a preliminary, small-scale algorithm test is an essential step before applying it into a real-world scenario. A pilot testing phase will help a business to amend the design to identify unnecessary costs and time expenditures, while also better understanding the data. As this was not sufficiently done in the Ofqual case, the algorithm simply did not provide the right answer to the problem it was trying to solve.

Championing ethical AI

More than ever, companies are realising one simple truth: failing to operationalise data and AI ethics is a threat to the bottom line. Missing the mark can expose companies to reputational, regulatory and legal risks. Here are some key areas that businesses should consider when leveraging AI models:

  • Usage consent: make sure that all the data you are using has been acquired with the proper consent
  • Diversity and representativity: AI practitioners should consider how diverse their programming teams are and whether or not they undertake relevant anti-bias and discrimination training. This will draw upon perspectives of individuals from different genders, backgrounds and faiths which will increase the likelihood that decisions made on purchasing and operating AI solutions are inclusive and not biased
  • Transparency and trust building: accurate and robust record keeping is important to assure that those impacted by it know how the model works

The ways AI can be utilised in the financial services industry is increasingly growing. An example is the use of document-centric identity proofing space whereby an identification document, such as a passport, is matched with a selfie of the user to confirm real and virtual identities. This will be an essential area of focus for financial services companies as they look to confirm that users are who they claim to be when the physical branch is diminishing. When analysing if a person is the same as the picture on their documentation, for example, a biased AI model can completely undermine the decision made.

However, it’s reassuring to see that the 2020 Gartner Market Guide for Identity Proofing & Affirmation predicts that by 2022, 95% of RFPs will have introduced clear requirements around minimising demographic bias. This demonstrates how organisations are now becoming more aware of the detrimental impacts that demographic bias in the performance of identity-proofing processes could have on their brand as well as being clear on the legal consequences they risk facing.

In turn, there is a real opportunity to leverage AI solutions to provide the best service, but financial institutions must ensure that they are doing so in an ethical, accurate, and representative way.

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