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LEVERAGING BIG DATA TO REVOLUTIONISE FRAUD DETECTION

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Karthik Krishnamurthy, Vice President, Enterprise Information Management, Cognizant

By Karthik Krishnamurthy, Vice President, Enterprise Information Management, Cognizant

Banking is a massive, complex industry with many facets—retail banks, credit card lenders, managed investing, risk management—all of whom approach fraud detection and prevention differently. Credit card fraud makes up around 40% of the total problem. According to Consumer Sentinel Network, U.S. Department of Justice, the total amount of credit card fraud worldwide is $5.55 billion and on the rise.

Karthik Krishnamurthy, Vice President, Enterprise Information Management, Cognizant

Karthik Krishnamurthy, Vice President, Enterprise Information Management, Cognizant

The sheer volume of loss attributed to fraud is pressuring financial services companies to devise solutions to prevent and identify fraud, while continuing to provide a positive and customized experience for an increasingly sophisticated customer.

In order to achieve a more accurate and less intrusive fraud detection system, banks and financial service institutions are increasingly investing to perfect the algorithms and data analytics technology used to spot and combat fraud. This technology uses large volumes of data being generated at a high velocity to increase confidence and accuracy in fraud detection.

Increasing Accuracy in Fraud Detection

As a highly customer-centric industry, banks and financial service providers need to make sure they are attacking fraud strategically and not disrupting their customers’ banking experience. The key to accurate and non-disruptive fraud detection is to implement emerging technology that allows banks to gain a holistic view of its customers.

This view of fraud detection uses data available from a variety of sources—mobile data, along with social data from Facebook and Twitter—and uses it to distinguish fraudulent activity from normal activity. For example, if a credit card customer fails to alert her card lender of her travel plans, a strategically implemented fraud detection system can enable the lender to automatically gain insight from mobile and social data that the customer is travelling and thereby reduce the incidence of false positives. With this additional insight, the lender’s algorithm may determine that the likelihood of fraudulent activity is very low, and the customer’s card activity should be allowed to proceed.

Using social data to ‘cluster’ information is another innovative approach. For example, if the cardholder’s social media contacts are tagged as co-workers and their social profiles indicate their current geographical disposition, a bank can use this information again to correlate recent charges incurred by the cardholder.

Machine Learning

An emerging and powerful aspect of big data and fraud detection is machine learning. In a nutshell, machine learning takes place when agile systems are configured to learn from one another. It discovers the patterns buried in data and learns from it to deliver higher quality insights, helping detect fraud in real time and adapting its systems to more quickly identify fraud in the future. This technology is revolutionary in fraud detection and banks and financial service companies implementing it are gaining a significant competitive advantage.

Machine learning models are increasingly being used to screen financial transactions for fraud. Text mining and machine learning technologies are effectively used to combine data from suspicious transactions with related extracts from other internal and external sources (such as social networks). One immediate benefit, as discussed earlier, is to effectively reduce the amount of false positive hits thrown out by the existing surveillance systems, thereby reducing the costs of manual inspection and making fraud detection more relevant and accurate.

Best Practices for Implementing Big Data

Banking and financial services institutions are increasingly aware of the benefits of leveraging big data in fraud detection, but often struggle with where to get started. The following best practices should be considered when developing a strategy:

  • Start with small and specific uses for big data: The first thing organizations should do is identify one or two business problems that can be resolved by improving fraud detection, and then dedicate the R&D resources to develop solutions. This type of ‘outcome-based thinking’ will ensure the business success of the initiative.
  • Ensure you’re working with high quality data: Take the time on the back end to ensure you are collecting the proper data and are separating the signal from the noise to allow for proper data analysis.
  • Know your regulatory environment: Understand the boundaries for using customer data and the relevant privacy laws. This will continue to be a challenge for organizations, but can be managed with the right approach.
  • Ensure IT and business units are collaborating: Implementing big data systems can be disruptive to an organization. Adopt destination-driven thinking where you and your team articulate and agree upon clear goals, and then evangelize your big data strategy across the organization to gain broad buy-in. These clear objectives will motivate teams to work through inevitable friction.

Looking Ahead

The beauty of big data is that it presents a new realm of possibilities for the financial services industry and, more importantly, will help organizations run differently. Technology will continue to advance and offer new strategies for optimizing fraud detection. Financial services organizations should ensure their internal teams are informed about trends and developments, or are partnering with experts who effectively help them stay ahead of the technology curve.

Big Data allows financial services firms to greatly enhance the speed of fraud detection and prediction using massive amounts of data from a hybrid of sources: point of sale, social media, customer databases, and external sources from data vendors. The analytic results from this data are growing into a collective “fraud database” for the financial services industry, which is driving new analytical models.

In the future however, Big Data will help deal with the globalization of fraud itself. On a global scale criminals are developing fraud tactics and scenarios driven by data and analytics.  They use data to probe for weaknesses and monitor the “success” of fraud programs they initiate. As computing resources become cheaper and faster in an internet world where a “location” is very much a virtual presence, criminal enterprises can move operations in a borderless digital world.

The challenges that the financial services industry faces with fraud have an enormous impact on customer service and the fight to lower fraud loss. Real-time analytics and machine learning built on top of a Big Data repository represent the solution platform for fraud detection and predictive/preventable fraud while maintaining a highly level of customer satisfaction.

Banking

AML and the FINCEN files: Do banks have the tools to do enough?

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AML and the FINCEN files: Do banks have the tools to do enough? 1

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.

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Banking

Local authorities and business networks play a key role in small business success, and must be protected during COVID rebuild

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Local authorities and business networks play a key role in small business success, and must be protected during COVID rebuild 2
  • 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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Banking

Do we really need banks? Yes, but digital transformation industry-wide is vital

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Do we really need banks? Yes, but digital transformation industry-wide is vital 3

By Charley Cooper is Managing Director at enterprise blockchain firm, R3

The Coronavirus crisis has taught us that we are capable of going digital quickly when we need to. As the banking sector faces a second wave, the ability for individual firms to grow and succeed will be reliant on better connectivity and efficiency at the industry-level, writes R3’s Charley Cooper.

The sudden and dramatic pace of change has been seen globally over the last six months. Decades of paper-based practices are being updated, digitised and overhauled as the whole word adapts to working online. As of today, countries are accepting “alternative arrangements” for original paper export certificates, New York is allowing notary services by video, and global banks are accepting “original” documents and acceptances by email.

Over the coming months, we will see this digital transformation extend from individual use cases and firm-level deployment to entire industries. And perhaps in no other industry is this more critical than in financial services, where the role of banks continues to be challenged because of the inefficiencies they face as a result of decades of siloed technology deployment.

While unquestionably an improvement over reliance on manual processes, regular “digital transformation” as implemented by a single bank has limited benefits. These typically include greater automation of business processes, acceleration in adoption of electronic channels, elimination of manual processes, standardisation of non-value-adding business practices and a focus on driving up data quality and speed of information flows.

Now consider achieving digital transformation at the level of the entire market, rather than on a bank-by-bank basis. Whilst a digital transformation project for a single bank might automate a business process between a front and back office, a digital industry transformation project might optimise the trading and settlement of the asset between buyer and seller and their custodians too.

Of course, such things have been attempted before. But there have been many failures and the successes are notable by how they have resulted in new dominant centralised providers – for example for market data, messaging or settlement. The advent of blockchain architectures showed us there was a new way to tackle the problem, one that worked with the grain of existing markets.

Done right, the prize is a huge “productivity dividend” as entire markets are unshackled from their analogue histories.

Tackling interbank reconciliation at the industry level

The Italian financial services industry provides a pertinent use case of digital industry transformation. 32 banks in Italy went live in March with one of the first real-world deployments of enterprise blockchain technology in interbank financial markets. 23 more banks went live in May, with further institutions scheduled to go live this autumn. Built by the Italian Banking Association, ABI, the Spunta Banca DLT app on R3’s Corda Enterprise platform tackles the market-wide issue of interbank reconciliation.

The traditional reconciliation process for interbank transactions in Italy—formerly governed by the “spunta” process— is notoriously complex. Resolving mismatches in transactions is a labour-intensive process, hampered by a lack of standardisation, fragmented communication and no “single version of the truth.” The Spunta Banca DLT app automates the reconciliation process and enables banks to pinpoint mismatches in interbank transactions quickly by sharing common data in a secure way.

Connecting such a large and diverse group of banks in a live environment to tackle a shared problem is a major milestone for digital transformation in the Italian banking sector, providing a glimpse into a brighter, more efficient and interconnected future for all financial markets.

Changing mindset

The current crisis has accelerated the launch of digital technology for many use cases across a diverse range of sectors, but those that stand the test of time will be developed with an industry-level mindset, not firm-level.

It is now clear that the age of inter-bank optimisation is over – the path forward from this crisis will be paved by software that focuses on adding real value for entire markets, connecting banks to overcome the biggest challenges they share as an industry.

Banks must adapt and start thinking about technology in new and innovative ways if they are to retain their critical role in the global economy.

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