By Sean O’Dowd, Global Financial Services Director at MapR
Over the past few years, banks and financial services institutions have increasingly looked towards the opportunities brought by big data. There is no shortage of frameworks and data lake reference architectures for how big data can help with regulations and compliance.
But how much is actually getting done and is it working?
Big data for compliance
Failures in customer reporting is the single most expensive compliance issue, costing the world’s top investment banks $43bn in fines over the past seven years. These fines, coupled with the cost of compliance, are drastically impacting banks – and will continue to do so. Especially while the average efficiency ratios of major banks continue to sit way too high, at well over 50 per cent in many cases.
And while much has been done to decrease costs, there are still areas where new data platforms can improve speed and automation, while driving operational costs down. Beyond costs, when correctly applied, big data can help banks reduce regulatory compliance risks and avoid potential problems in real time.
But how? Let’s just look at some basics that modern data platforms should be able to handle for compliance purposes:
- Speed: institutional banking clients – as well as auditors – are demanding to see risk and possible exposure scenarios at increasingly regular intervals. Real-time analysis is a critical aspect here, coupled with scalability in production environments to handle high volumes of data, so big data platforms can help move large data fast.
- Archive: regulators simply want financial services institutions to hold more data and for longer. Banks must also use analytics to understand the integrity of that data – including voice data. And remember, if half a phone call is missing come audit time, it does not fully comply.
- Complexity:the increasingly complex, global assets being traded and held in portfolios contain not just more data – such as payments, fixed income, and derivatives – but also a greater amount of unstructured data, which big data platforms can then translate.
- Cost:with such high legacy costs and more personnel going to regulatory and financial reconciliation, firms have to comply at a lower total cost of ownership (TCO). These regulations and the market environment have greatly hampered banks’ abilities to just throw money at the problem. There is a high degree of automation that can be brought to bear with simple machine learning rules as an example.
- Agility:as regulations continue to morph, so do markets, and the data platform must be able to adapt more rapidly.
With these requirements of big data platforms– and many more besides – it is important to use a platform that can easily ingest data from both new and legacy sources.
And as banks look to maximise the value of their data, by feeding it into a variety of application and analytical models, both internal and external data sources should be used to consider these models from a regulator’s perspective. Too often the client fines that are hitting these banks are due to investor protection, transparency, and transaction accuracy – including misleading communication or fraudulent agent activity.
Limitations of current deployments
In many organisations, big data is not yet being fully utilised for compliance and regulations. Greater trust in data quality, lineage, and metadata management solutions are needed to reap the potential of big data platforms.
Moving from big data 1.0 to 2.0 solutions that are hardened for the enterprise is one way that businesses can achieve this. While many early to market providers and projects were initially good for simple offload and testing, real business applications require different standards.
This is why, for example, you see the adoption of Apache Spark rapidly pulling ahead of MapReduce. The market has realised the value, and different projects and solutions have come to market to meet the enterprise-challenge since these early solutions.
But beyond the technology deployed, one great challenge is that big data is not currently widely being used for broad-based, multi-asset compliance across multiple portfolios. Instead, it is happening in silos – either on an asset base or line of business level. And, on the occasions where it is being used across multiple assets, it is rarely deployed for reporting, which ultimately is what’s necessary to enable compliance.
Where is it going wrong?
This siloed mentality largely stems from process and political hurdles. There is great hesitancy around the move from the testing to the operational phase on a large scale for more critical applications. And this is where utility-grade solutions are paramount.
But instead of moving towards the heavier computation and real-time capabilities of these enterprise- grade data platforms, we’re continuing to see banks fall short on the use of such platforms to enable greater usage across assets, and for managing finance and risk.
With the information stored in silos and not deployed for larger analysis and reporting, banks remain forced to rely on manual intervention for timely reporting. And this ultimately leads to greater risk and exposure to human error.
But while there is considerable opportunity to drive greater implementation of new platforms to help firms with the current pace of compliance at banks, many banks are still making great strides here. And with new platform solutions now offering improved compliance data management, stress testing analytics, and historical data records keeping, there is an even greater incentive for using big data.
For banks to successfully digitise, however, they need money to clear risk hurdles and ensure better coordination across all lines of business – even if they’re not client facing. Ultimately, this will enable them to deploy the big data being collected more effectively to reduce regulatory compliance risks.
The resistance is not unlike what had been seen with cloud solutions as its adoption moved through the maturity curve. Senior data and IT execs need the confidence that these platforms can deliver. And rightly so, as their careers and the resiliency of the banks’ systems are riding on these platforms.
Some of the keys to moving forward:
- Banks need to adopt open source frameworks that allow more rapid adoption and testing of new solutions.
- Look for providers whose new big data platforms are being used for client facing applications, which are some of the most demanding and offer operational proof points.
- Data platforms should look to resolve legacy and silo pain points, not repeat them.
- There are vastly improved data governance solutions for data lakes that can adopt data standards (ontologies), leverage existing data models, rapidly bring structure to new incoming sources. and provide provenance that is required for compliance
Ultimately, it is a matter of gaining confidence that the new breed of data platforms is prepared to manage the workload. It’s also a matter of working with these providers to dial in the requirements and criteria that will be needed, and to fit those into the existing infrastructure.
Mastercard Delivers Greater Transparency in Digital Banking Applications
- Mastercard collaborates with merchants and financial institutions to include logos in digital banking applications
- Research shows that ~25% of disputes could be prevented with more details
As more businesses turn to digital payments, and the number of connected devices grows, one thing is becoming increasingly clear: consumers are demanding more clarity around what they bought and who they bought it from.
Most everyone has experienced the frustration of trying to decipher confusing and brief purchase descriptions when reviewing online statements. This confusion forces cardholders to contact their banks unnecessarily to dispute unrecognized transactions, adding extra steps for consumers and generating an array of costs for merchants and banks.
A new initiative from Mastercard and managed by Ethoca, the company’s collaborative fraud and dispute resolution technology, aims to eliminate this confusion and improve the customer experience. All merchants are encouraged to visit www.logo.ethoca.com and upload their logos for inclusion in online banking and payment apps. The merchant logos will be linked to corresponding transactions, adding clear visual cues to help cardholders quickly identify legitimate purchases. Participating merchants are provided an opportunity to simultaneously extend their brand presence as well as eliminate expensive and time-consuming chargebacks. This program is also available to all financial institutions.
A recent Ethoca-commissioned Aite Group study of the US market revealed that 96% of consumers want more details that help them easily recognize purchases, and nearly 25% of all transaction disputes could be avoided by delivering these details – including logos. It’s estimated that global chargeback volume will reach 615 million by 2021, fueled in large part by frustrated consumers turning to the dispute process unintentionally.
“With greater digital dependency, having real-time purchase details is critical for consumers, merchants and card issuers alike,” said Johan Gerber, executive vice president, Cyber and Security Products at Mastercard. “We continue to collaborate with industry partners to bring clarity and simplicity before, during, and after transactions. By enriching transaction details, merchants can alleviate friendly fraud, reduce chargebacks and improve the customer experience.”
This endeavour is part of comprehensive efforts to deliver the most efficient, safe, and simple payment experience from the minute a consumer begins browsing to once they’ve made the purchase. This includes Click to Pay, Mastercard’s one-click checkout experience, to the integration of biometrics to secure both digital and physical transactions, and Ethoca’s full suite of consumer digital experience solutions.
AML and the FINCEN files: Do banks have the tools to do enough?
By Gudmundur Kristjansson, CEO of Lucinity and former compliance technology officer
Says AML systems are outdated and compliance teams need better controls and oversight
The FinCEN files have shown that it’s time for a change in AML. We must take a completely new approach in order to catch up with the speed of innovation in financial crime.
Despite what you’ll read in news headlines, we can’t lay all of the blame for anti-money laundering failures at the doors of the banks. The majority of compliance teams are doing what they can, and what they are being asked to do.
Historically, AML has, in large part been a box-checking exercise. Banks have weaved through mountains of false alerts, investigated cases, sent SARs, and then got on with business as usual. In some jurisdictions, banks can‘t even interfere with customers under investigation, in fear of jeopardizing cases.
But the sentiment towards banks’ responsibility in AML is changing. They are increasingly looking at AML as a corporate social responsibility issue and even a competitive advantage. Banks are looking to protect their brands from the horrors of an AML scandal, and as such are taking a more proactive approach.
They are also throwing a lot of money at the problem. Deutsche Bank claims to have invested close to $1 billion in improved AML procedures and increased its anti-financial crime teams to over 1,500 people. Most big-brand banks have a similar story to tell.
With reputation on the line, better AML controls can become good business.
So where does the problem lie?
From the thousands of SARs discovered in the FinCEN files, lack of customer oversight is evident. Banks need to establish a method of knowing their customers through their actions across the organization and beyond the organizational walls. By doing so, banks can better understand AML and compliance risk, which gives them the necessary tools to bar customers from doing business or limiting their activity.
While banks are striving to better enforce regulations by pouring money and resources into CDD and transaction monitoring, forming this type of intelligent customer overview might be the real solution. Proper Customer Due Diligence and customer risk monitoring can only be achieved by continuously tracking customer behaviour and transactional networks. With the latest developments in Artificial Intelligence – that is now possible.
But, the reality for compliance teams is they are hindered by outdated technology in their risk assessment and transaction monitoring systems and because of this, banks are fighting a steep, uphill battle against serious organised crime.
In 2019, the Bank of England issued a statement that claimed: “existing (money laundering) risks may be amplified if governance controls do not keep pace with current advancements in technological innovation.”
I know from my time working as a senior compliance technology officer that many traditional AML systems are inefficient, slow and labour intensive, and often lead to inaccurate outcomes. In fact, most of the systems pre-date the iPhone, so they are using last-generation technology and techniques to detect criminal activity.
In short, legacy AML systems are not fit-for-purpose. Legacy vendors built them for the box-checking world of the past, and they are focused on one suspicious transaction at a time – rather than looking at ‘bad actors’ in the financial system, and patterns in their behaviour.
As launderers constantly evolve their techniques to circumvent rule-based or simple statistical detection, the AML systems market has not kept up. There is a dire need for innovation.
Unless systems are updated, banks can continue to file suspicious activity reports (SAR), but if bad actors can conduct their business ‘as usual’ and shuffle money around the globe to hide its malicious origin, the effectiveness of a SAR is significantly diminished.
What’s the solution?
I believe we need to rethink our entire approach to AML. We need to empower compliance departments with better controls and oversight, and move away from outdated, traditionally rule-based systems and towards a modern, AI-enabled, behavioural approach.
While the bad guys have learnt how to evade rule-based systems, they find it extremely difficult to get around AI algorithms that search for anomalies in behaviour. The advancement of AI algorithms, especially in the field of deep learning, provide an opportunity for banks to detect more complex and evasive money laundering networks.
So the answer is to establish continuous automated risk monitoring and implement a workflow system that provides money laundering risk scores for customers.
The latest AI software could kickstart a new age of customer AML risk-based overview. Instead of relying on static and self-reported KYC data, AI systems can analyse behaviour over a period of time and compare it with peer-groups and past actions. It provides compliance teams with a continuous risk-rating of their customers, actor insights and summaries to facilitate efficient and thorough investigations, and an organizational-wide overview.
Recent advancements in AI have not only made the above possible, but also practical. Our latest Human AI models contextualize and explain the appropriate data, making it easier for banks to spot sophisticated crime.
By looking at AML not simply as a box-ticking exercise, but as a competitive advantage that can increase customers’ trust in their financial institutions, banks have a lot to gain. Moving towards behaviour-based AML systems is a move towards making money good.
Local authorities and business networks play a key role in small business success, and must be protected during COVID rebuild
- 23% of UK’s top performing businesses have been supported by local enterprise partnerships and growth hubs
- Similarly, 30% of Britain’s strongest businesses have obtained external finance in the last 3 years
- New findings come as part of an independent, holistic study into small business success, commissioned by Allica Bank to support British businesses
A new study, commissioned by business bank, Allica Bank, shows that a high level of engagement and interaction with external institutions and resources, is central to SMEs’ prospects of success.
The study analysed data from over 1,000 companies and ranked their success on a scale that evaluated factors including productivity, growth, consistency and outlook. To measure SMEs’ external engagement, survey respondents were asked whether or not they had engaged with local enterprise partnerships, growth hubs, or external financial advisers, as well as whether they had obtained credit or sought re-financing advice, in the last three years.
The benefit to small businesses in making the most of external resources are clear to see, with a quarter (23%) of the UK’s top performing SMEs – those in the top tenth percentile – actively engaging their local enterprise partnership or growth hub in the last three years. This compares to just 16% of all other small businesses. With such a clear benefit to businesses, these external networks must not only be protected but prioritised by any Government plans to rebuild the economy post-COVID.
Similarly, of the top performing SMEs in the country, 30% have obtained external credit in the past three years, compared to less than a quarter (24%) of all other businesses. This figure drops even further for the weakest performing businesses – those in the ninetieth percentile – where just 12% of businesses have obtained external financial support in recent years.
Chris Weller, Chief Commercial Officer, Allica Bank, said:
“At Allica Bank we understand that no two businesses are the same. We also know that no-one knows a business as well as its owners and managers. But they can’t be expected to be experts on everything.
“In the UK there is a wealth of external advice and support for small businesses and we urge each and every business out there to tap in to the external resources around them. Third-parties, such as business clubs, chambers of commerce, local enterprise partnerships and trade bodies, can be invaluable sources of advice and further resources. And although they have excelled in their given field, business owners may still lack knowledge in many other areas of running and growing a business. Therefore, engaging with third parties can give business owners the kinds of insight – and fresh perspectives – they need to succeed.
“As the economy and the country comes to terms with the impact of the COVID-19 pandemic, it is important these vital SME resources are protected and given the funding they need to continue providing invaluable insight and support to small businesses up and down the country.”
Allica Bank’s SME Guide to Success identified six ‘rules to success’ that were more likely to be displayed by top-performing SMEs compared to their counterparts. The full report contains a wealth of additional data and insight into each of these topics.
As part of its mission to empower small businesses, Allica Bank is making the findings freely available and running a series of free online workshops with relevant partner organisations for businesses to attend.
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