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Banking

DETECT AND PREVENT BANKING APPLICATION FRAUD

By Dan Barta, Principal Solutions Architect, SAS Global Security Intelligence Practice

 

Analytics and artificial intelligence uncover the real challenge – synthetic identities

The financial services industry won’t soon forget the headlines of 2013, when a 63-year-old New Jersey jeweller was charged with leading a fraud ring that colluded with merchants getting kickbacks. One of the largest credit card fraud schemes prosecuted by the US Department of Justice, this one involved more than 7,000 fake identities and 25,000 fraudulent credit cards obtained through banking application fraud, resulting in more than $200 million in losses.

Early-detection analytics can redefine the odds of discovering banking application fraud. For example, in our work here at SAS with an Asia Pacific bank, network analysis found:

  • 60,000 contact phone numbers referencing immigration agents.
  • 5,000 contact numbers referencing casinos.
  • 2,500 phone numbers referencing the bank branch at which the application was made.
  • 1,500 numbers referencing a meat processing plant.

These signs pointed to fraudsters flying below the radar with high-volume, low-value credit applications. Using SAS® software, the bank found four times more banking application fraud, valued at $3 million a month, compared to its former techniques.

The analytic methods that help detect banking application fraud also apply for other credit-granting organisations, such as telcos, online retailers and auto finance organisations.

Synthetic identities: The gold standard for banking application fraud

Fraudulent applications can start with stolen or manipulated identities, but the preferred approach is to use synthetic identities – a combination of fabricated credentials not associated with a real person. Hence, no one complains about a new unauthorised account, credit card or line of credit.

This dissociation from a real person makes banking application fraud with synthetic identities particularly attractive to fraudsters – and more challenging to detect. Gartner estimates that synthetic identities are behind 20 percent of credit charge-offs and 80 percent of credit fraud losses.

A common pattern in the US is to create fake Social Security numbers or get them off the “dark web” – preferably SSNs of the deceased or children, not likely to notice unauthorised credit activity. Once an identity is created, the fraudster builds the appearance of a real person by:

  • Applying for credit, which triggers a credit bureau record whether the identity is real or not.
  • Adding the synthetic identity as an authorised user on an existing account, which may itself be a sham.
  • Getting merchants, real or fake, to collude in creating bogus credit accounts and credit bureau reports.

For all three approaches, the underlying theme is the same: The fraudster exploits the services of the credit industry – banks, other creditors and credit bureaus – to build a credible identity to gain access to yet more credit. That’s banking application fraud at its core.

 

How to detect bust-out fraud

That New Jersey ringleader and his associates had honed the art of a bust-out scheme: open a line of credit for a fake identity, cultivate a good history for that account, then grab the big payoff. In insider terms, it’s “make up,” “pump up” and “run up and cash out.”

Analytics and machine learning are empowering banks and other creditors to fight back more effectively – and earlier in the game. The trick is that the best analytical methods will vary depending on available data, the type of fraud and the phase of the endeavour. Multiple methods used together can very effectively find fraud while managing false positives.

At the make-up stage, the fraudster manufactures identities and uses them to gain access to credit. Financial institutions can spot the seeds of future fraud by:

  • Monitoring application data to see if the same information or device is being reused across multiple identities that otherwise appear unrelated.
  • Assessing past experience for existing or closed accounts that shared the same data element, such as device ID, address or SSN.
  • Searching for “proof of life,” well-rounded details for the identity, such as driver’s license, voter registration or property ownership.
  • Analysing the social network to spot unusual or suspicious connections (or lack thereof) among applicants, devices, accounts, credit files and application data.

 

At the pump-up stage, the fraudster uses credit lines in a normal fashion, making small purchases and paying off the account each month, thereby creating the appearance of good credit, which is used to request further credit.

Even though the fraudster is building a good credit file, there are ways to identify suspicious or high-risk activity on these accounts through rules and models. For example:

  • Are payments from the same source (bank and account) being used to pay otherwise unrelated accounts?
  • Is the same device being used to access and/or make payment on what appear to be unrelated accounts?
  • Are credit lines fully used soon after account opening?
  • Is the bank offering the credit line increases, or are the requests coming from the “customer”?
  • Given the demographic data on the credit application, would the credit-holder be likely to purchase from the type of merchants where the account is being used?

 

At the run-up and cash-out stage, the fraudster (or organised fraud ring) maxes out the cards and disappears. In some cases, the fraudster will make a final payment with a counterfeit check and quickly max out the accounts before the bank realises the payment is worthless. This results in an even higher loss than the credit limit on the card.

It would be optimal to uncover the scheme before the run-up and cash-out stage. Rules and models can detect late-breaking indicators, such as:

  • Increased transaction frequency.
  • Repeatedly maxing out a credit line and paying it off in full without carrying a balance.
  • Payment on a card significantly before the payment due date.
  • Payment by check when prior payments were made online.
  • Network association with other accounts showing high-risk activity.

If a charge-off occurs, forensic analysis of the account can help you tune the rules and models for ever-greater precision and support smart collection efforts. You can use the experience from previous scenarios as inputs for unsupervised or supervised machine learning, where the algorithm finds and learns from patterns in the data. By uncovering what you didn’t know to look for, machine learning has been shown to detect more fraud, even rare events that don’t follow common patterns.

On the positive side, fraud detection analytics can also affirm legitimate applications that can then be fast-tracked to approval for a more positive customer experience, lower friction and fewer abandonments. With better application screening, good customers get expedited service, and bad ones are detected before they cash out.

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Banking

SEIZING THE OPEN BANKING OPPORTUNITY

Nick Maynard is a Lead Analyst at Juniper Research

 

Open Banking has made significant progress in 2020, having recently launched across much of Europe and now starting to emerge in other markets too. And there are two primary reasons why Open Banking is disrupting the banking industry so much:

  • Banks have begun to discover the real competitive advantage of a more open approach to banking. Offering a superior Open Banking experience to customers can be a compelling differentiator from other competitors as part of a wider digital app experience. Open Banking also creates a level playing field in markets where regulatory intervention has led to Open Banking deployment. As all banks are required to deploy APIs in this scenario, the situation is the same and does not put any one particular bank at a disadvantage.
  • Legislation – for example, in October 2015, the European Parliament adopted PSD2 (the revised Payment Services Directive). By early 2020, major banks in the EU had adopted Open APIs. There have however been many cases of late deployments of APIs and problems with the availability of APIs.

 

Nick Maynard

The Disruption Factor

Open Banking is a major disruptive factor for banks. The reason for this being that it opens up account data to both AISPs (Account Information Service Providers) and PISPs (Payment Initiation Service Providers), which can attempt to carve out a role in the banking area.

  • AISPs: These new vendors are able to access transaction data and balance information, as well as related information. This has, in particular, led to the rise of vendors such as Emma, Yolt and Connected Money. These vendors combine information from multiple sources, adding value to the user.
  • PISPs: In this case, the vendors are able to leverage Open Banking API connections to initiate payments directly from the bank accounts in question. This means that these players are able to bypass traditional payment methods, such as cards. Vendors such as American Express and PayPal have already launched solutions that have taken full advantage of this action.

 

PSD2 Changes

Generally, the implementation of the new PSD2 European regulation for electronic payment services effectively reduces the entry barriers for new digital players. It also opens up banks to the potential for competition, enabled by their own APIs. This allows these players to compete with existing services in fields currently offered by the banks. In the case of AISPs, it is possible that third-party applications could displace the role of the apps from incumbent players, which would dilute the bank’s relationship with their users.

As with any fundamental change to markets in the banking area, there is the potential to bring a number of both opportunities and challenges to consider with Open Banking.

Open Banking Opportunities & Challenges to Consider

Source: Juniper Research

Banks and other parties that are looking to become involved in the Open Banking ecosystem must weigh these opportunities and challenges carefully. Open Banking certainly needs a more collaborative approach than traditional banking models, which will require significant effort to make them successful.

 

The Forecast for Open Banking

The total number of Open Banking users is set to double between 2019 and 2021, reaching 40 million in 2021 from 18 million in 2019. The ongoing Coronavirus pandemic is increasing the need for consumers to have the clarity of combining their accounts and gaining insight on their financial health, and also boosting momentum in the adoption of Open Banking.

This extraordinary growth is being driven by Europe, where the regulator-led approach to Open Banking has created a standardised market, with low barriers to entry. This contrasts with markets like the US, where a lack of central regulatory intervention is limiting growth potential.

 

Open Banking – Delivering Opportunities and Threats

It is worth noting that Open Banking can be both a threat and an opportunity for traditional banks. While Open Banking exposes user information and access to potential competitors, this threat has the potential to affect all players in the market equally. Consequently, established banks must create innovative Open Banking services that will provide benefits for the user, while also attracting customers from less innovative competitors.

Payments will be critical to the emerging Open Banking ecosystem; accounting for over $9 billion in transaction value in 2024. However, payments in this ecosystem are at a particularly early stage. While eCommerce is dominated by card networks, there is the potential that this role will be eroded over time by ‘direct from account’ payments. Consequently, card networks should look to offer Open Banking-enabled payment services, in order to offset the risk of future disruption.

Open Banking Users in 2021 (m), Split by 8 Key Regions: 40 Million

Source: Juniper Research

 

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Banking

2021: THE NEW-NORMAL LIFECYCLE FOR BANKING

Laura Crozier, Global Director of Industry Solutions, Financial Services at Software AG

 

It would be impossible to talk about predictions for the banking industry in 2021 without mentioning the cataclysmic impact that 2020 and the pandemic has had on people, businesses and countries.

Unlike with the global financial crisis, banks have been able to step up as “good guys” this time around, rebuilding their reputations as well as accelerating digital transformation. One of the main outcomes is increasingly smart, efficient online payments.

In 2020, the banking industry innovated like never before. This is the new normal. Overall, customers and society will be the beneficiaries from the changing industry. Here are my predictions:

 

Reputations are reborn

Banks across the globe pulled out the stops to integrate and adapt systems and processes to help customers during the pandemic. They offered accommodations in loans, assisted governments with the distribution of financial relief, and supported consumers by upping contactless spending limits and virtual deposits.

In 2021, banks will risk losing that rosy glow as economic circumstances drive them to deal with non-performing loans, mortgage foreclosures, layoffs etc. But, beyond their role in society as providers of capital and liquidity, banks will invest to sustain their reputations as trusted and good corporate citizens and use their power to persuade their customers and providers to adopt higher environmental and ethical standards. This will be in the areas of bank carbon-neutrality, sustainable financing, serving the unbanked, diversity and gender equality (as the number of women running a major global bank will double from one (Jane Fraser at Citi) to two). It’s a start.

 

Coming of age in the way of working

Back in Q1, when bank employees cranked up their laptops on their dining room tables, banks that were strategically undertaking business transformation accelerated their efforts. Those that were tactical, or on the fence, now understand with painful clarity that this work must be undertaken strategically.

Cracks in process and the way of working and their resulting risks can be crippling. Especially from a back-office perspective, it is not enough to rely on “organisational memory” and collegial proximity for work to get done right. Advanced banks pushed the boundaries of remote work, and the proof of concept was successful. So, they’re doubling down on developing digital twins and moving to the cloud. They’re adopting the hybrid office/WFH approach to reduce health risks and reduce cost permanently. The watercooler will never be the same.

 

The death of cash

Ok, maybe the rumours of the death of cash are a bit exaggerated since there will always be the need for cash (and, to some extent checks; the USA, for example, cannot seem to live without them). But the pandemic has permanently changed the way that consumers and small businesses bank, and the demotion of cash has been accelerated by a decade by the pandemic. For example, the Norwegian central bank said that cash payments in that country have plummeted to just 4% of transactions since March.

Implications? It will be critical to continue evolving payments to be smart, safe and flexible to compete in new world, in both retail and commercial banking. Also, the permanent change in the mix of channels will see banks’ face-to-face engagement with customers fade. Branches aren’t going to go away entirely, but they will be reserved for high value activities – by appointment only. To compensate, the personal touch has to be delivered digitally and intelligently.

The role of the bank as a “financial wellness partner” is being born. Banks will use customers’ data, not just to personalise and differentiate banking experiences, but to make recommendations for products and services beyond traditional banking from across their ecosystem to serve their customers well. Just as customers own their cash (physical or digital), in the future they will demand that they own their data (and can share it with whom they choose). Then retail and commercial clients will share their data in return for value.

 

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