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Google releases Model Card Toolkit to promote AI model transparency

July 30, 2020 Big Data

Google today released the Model Card Toolkit, a toolset designed to facilitate AI model transparency reporting for developers, regulators, and downstream users. It’s based on Google’s Model Cards framework for reporting on model provenance, usage, and “ethics-informed” evaluation, which aims to provide an overview of a model’s suggested uses and limitations.

Google launched Model Cards publicly over the past year, which sprang from a Google AI whitepaper published in October 2018. Model Cards specify model architectures and provide insight into factors that help ensure optimal performance for given use cases. To date, Google has released Model Cards for open source models built on its MediaPipe platform as well as its commercial Cloud Vision API Face Detection and Object Detection services.

The Model Card Toolkit aims to make it easier for third parties to create Model Cards by compiling the necessary information and aiding in the creation of interfaces for different audiences. A JSON schema specifies the fields to include in a Model Card; using the model provenance data stored with ML Metadata (MLMD), the Model Card Toolkit automatically fills the JSON with information including data class distributions and performance statistics. It also provides a ModelCard data API to represent an instance of the JSON schema and visualize it as a Model Card.

Google releases Model Card Toolkit to promote AI model transparency

Above: An example of a Model Card.

Image Credit: Google

Model Card creators can choose which metrics and graphs to display in the final Model Card, including stats that highlight areas where the model’s performance could deviate from its overall performance. Once the Model Card Toolkit has populated the Model Card with key metrics and graphs, developers can supplement this with information regarding the model’s limitations, intended usage, trade-offs, and ethical considerations otherwise unknown to model users. If a model underperforms for certain slices of data, the Model Cards’ limitations section offers a place to acknowledge that along with mitigation strategies to help address the issues.

“This type of information is critical in helping developers decide whether or not a model is suitable for their use case, and helps Model Card creators provide context so that their models are used appropriately,” wrote Google Research software engineers Huanming Fang and Hui Miao in a blog post. “Right now, we’re providing one UI template to visualize the Model Card, but you can create different templates in HTML should you want to visualize the information in other formats.”

The idea of Model Cards emerged following Microsoft’s work on “datasheets for datasets,” or datasheets intended to foster trust and accountability through documenting data sets’ creation, composition, intended uses, maintenance, and other properties. Two years ago, IBM proposed its own form of model documentation in voluntary factsheets called ” “Supplier’s Declaration of Conformity” (DoC) to be completed and published by companies developing and providing AI. Other attempts at an industry standard for documentation include Responsible AI Licenses (RAIL), a set of end-user and source code license agreements with clauses restricting the use, reproduction, and distribution of potentially harmful AI technology, and a framework called SECure that attempts to quantify the environmental and social impact of AI.

“Fairness, safety, reliability, explainability, robustness, accountability — we all agree that they are critical,” Aleksandra Mojsilovic, head of AI foundations at IBM Research and codirector of the AI Science for Social Good program, wrote in a 2018 blog post. “Yet, to achieve trust in AI, making progress on these issues will not be enough; it must be accompanied with the ability to measure and communicate the performance levels of a system on each of these dimensions.”

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Big Data – VentureBeat

ProBeat: Alphabet’s Sidewalk Labs won’t be the last to play the coronavirus card

May 8, 2020 Big Data

Yesterday, Alphabet’s Sidewalk Labs killed its Toronto smart city project. raincoats designed for buildings, heated pavement, and object-classifying cameras will not be traded for unprecedented data collection. Privacy advocates celebrated — Big Brother would not be gaining even more invasive power to surveil residents. But this story is far from over. Whether one hoped for a smart city or feared it, the reality is this project did not live or die on its merits. Nor did it get axed because of “unprecedented economic uncertainty,” as Sidewalk CEO Daniel Doctoroff suggested. The pandemic was just the scapegoat.

The rest of 2020, and possibly beyond, is going to be filled with stories about companies pulling back due to the economy. Look out for them, because they are going to be instructive of what were the riskiest bets in the first place. If you run a business, it might be time to rip off the Band-Aid yourself.

Pandemic or not, it is always instructive to follow the money. Sidewalk Labs is a Google sister company under the Alphabet umbrella. Other Alphabet companies include Calico, CapitalG, DeepMind, GV, Google Fiber, Jigsaw, Loon, Makani, Verily, Waymo, Wing, and X. These moonshots are not broken out in Alphabet earnings because frankly, none are profitable. Instead, they are lumped together under a line item called “Other Bets.” Last week, Alphabet reported its Q1 2020 earnings during which Other Bets revenue was down 21% to $ 135 million, while losses were up 29% to $ 1.1 billion. Yes, Other Bets burned eight times more cash than it generated.

Q1 2020 was a special quarter for Alphabet. Not because it was the worst quarter for Other Bets — there have been worse ones, if you can believe it. Not because it overlapped with the pandemic — Alphabet seems to be handling the downturn, so far. Q1 2020 was special because it was the first full quarter in which Sundar Pichai oversaw Alphabet.

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In December, when Google’s CEO also became Alphabet’s CEO, I explained we knew what to expect: Alphabet companies will either become more focused or get folded into Google. Maybe Pichai has made a decision about Sidewalk Labs. Maybe he hasn’t. Either way, the Sidewalk Labs project was low-hanging fruit — the ROI for a Google smart city was never there.

Don’t get me wrong. I was critical of the Sidewalk Labs approach and have generally argued that tech company expansions need to be less arrogant and more transparent. It’s one thing for a company to be able to halt development of an app overnight. It’s completely another to walk away from building a smart city overnight. Imagine if the smart city already had residents living in it and Sidewalk Labs decided to pull the plug. Is that really the type of control we want to hand over to tech giants?

And yet, Sidewalk Labs’ withdrawal from Toronto is not democracy thwarting surveillance capitalism. Soon after the news broke, the government-backed agency Waterfront Toronto stated “this is not the outcome we had hoped for.”

This case had more to do with the chickens coming home to roost at Alphabet; the pandemic was just the excuse. Keep an eye out for other companies using “unprecedented economic uncertainty” as cover to cancel projects, leave markets, and/or pivot — regardless of whether you are cheering for them or not.

ProBeat is a column in which Emil rants about whatever crosses him that week.

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Big Data – VentureBeat

SPDB Credit Card Centre Automates Early Collections

July 20, 2019 FICO

Shanghai Pudong Development Bank (SPDB) Credit Card Centre (CCC), one of the leading credit card issuers in China with more than 40 million cards issued, has used FICO® Customer Communication Services (CCS) to boost collections performance while driving down business costs.

Recent years have seen a rapid development of SPDB’s credit card market, an expanding scale of assets and stricter regulatory requirements on its collection business. In addition, the lender was also experiencing challenges to collection work and risk control due to human resource limitations. So, to improve the risk management of its business and break through the limitations of traditional human collection agents, SPDB looked to intelligent automation and introduced FICO’s CCS system in 2016.

CCS uses intelligent two-way communications such as phone calls, SMS and email to connect with customers with the right message at the right time.

CCS allowed SPDB CCC to tailor different treatment strategies to different groups to increase its effectiveness in debt collection, especially with early delinquency customers. The solution was also used to apply champion/challenger tests to explore more scientific and effective collection strategies and optimize them. Each collection approach was continually improved through analyzing the results and tweaking the strategy using machine learning. This was imperative to make sure the portfolios were being effectively managed during SPDB’s growth period.

At the same time, the SPDB Credit Card Center has saved significant labor costs using automated outbound collections. The collection business now runs its operations using 210 less staff per month, a 30 percent reduction, which has significantly cut collection operating costs, management costs and risk costs. The success of this project has allowed SPDB to reshape its business using Big Data and machine learning to improve efficiency and reduce risk.

During the past three years of continuous model and strategy optimization and iteration, CCS has helped SPDB to fully realize automatic intelligent collection for early delinquency customers and to effectively identify and prevent the risk of non-performing loans at an early stage.

“SPDB continues to use big data, machine-learning and AI with confidence,” said Sandy Wang, general manager for FICO in China. “The bank had already embraced these technologies for scoring, so it was a sensible extension to deploy them for customer collections as well. For their innovation with CCS in modernizing and digitize their banking services SPDB won our 2018 FICO® Decisions Award for Debt Collection.”

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UBS Card Center Wins Security Innovation Award with FICO AI

May 12, 2019 FICO

Congratulations to UBS Card Center on winning the Security Innovation of the Year award at the Retail Banker International Awards. UBS Card Center’s fraud team used the latest artificial intelligence and machine learning capabilities in the FICO^® Falcon^® Platform to stop 84 percent more fraudulent transactions last year than in 2015.

“We are combining our deep expertise in analyzing fraud trends and the latest breakthroughs in AI and machine learning from FICO to keep losses and false-positives low and customer satisfaction high,” said Marcel Drescher, Head of Fraud Services, UBS Card Center (shown above with Manuela Spillmann). “We are gratified by this industry award recognizing our success in stopping fraud.”

UBS Card Center processes roughly 25 percent of all credit cards in Switzerland. The need to optimise costs in the face of fierce competition meant UBS Card Center had to keep fraud write-offs to the very minimum. They were facing new fraud attack volumes but needed to uphold the highest standards for customer experience and satisfaction. This required the use of machine learning to minimize consumer interruptions while investigating more potential cases of fraud, all without adding staff.

To tackle this multi-dimensional problem, fraud experts at UBS Card Center used the free-form rule writing within the FICO Falcon Platform to create complex rules that deployed multiple AI techniques, including adaptive analytics. Adaptive analytics use the results of recent fraud investigations to automatically fine-tune the underlying neural networks in order to accurately reflect the latest fraud landscape. These custom rules, combined with the advanced analytics, were the only way to improve false-positives and fraud detection rates.

Using FICO AI and machine learning, UBS Card Center managed to investigate and resolve 42 percent more fraud alerts without bringing in new staff resources.

“UBS Card Center has reduced the amount of fraud write-offs per compromised card, stopped more fraudulent transactions and mitigated false-positive rates using the FICO Falcon Platform,” said Douglas Blakey, Group Editor Consumer Finance at Timetric Financial Services, which hosted the awards.

You can read more about UBS Card Center’s achievements and Security Innovation award in this Electronic Payments International article.

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Recurring Payments: Storing Card Holder’s Data Safely

April 2, 2019 Microsoft Dynamics CRM

By David Goodale of Merchant-Accounts.ca

One of the biggest concerns you hear when setting up modern payment systems is about security. How can we safely store a card holder’s data securely? In part one of this three-part article on recurring payments, we look at storing cardholder data.

Storing Cardholder Data (Avoid when possible!)

When e-commerce payments were still a relatively new concept there was no standard for the protection of cardholder data. This left merchants to, more or less, do what they wanted when it came to handling card data. Of course, breaches occurred causing headaches for cardholders, merchants, issuing banks, and the card brands.

When card data was stolen or compromised it led to lost funds, as well as the cost of creating and re-issuing the stolen cards. In 2004 the card brands worked together to address this with the advent of the PCI standard. The creation of this security standard has provided the benefit of a clear guideline that merchants can follow for the protection of cardholder data, but it’s created a tremendous challenge for merchants to deal with.

The Payment Card Industry Data Security Standard is a complex, technically challenging standard that even large and technically sophisticated organizations can find challenging. For regular businesses, it can be intimidating or perhaps in some cases outright impossible to comply with.

One of the best ways to minimize the headache is to avoid holding the data. In general, you want to avoid storing sensitive data whenever you can avoid it. (Unless you have strong technical expertise within your organization, or perhaps just an unhealthy appetite for unnecessary liability.)

The PCI security standard appreciates and takes into account that some businesses simply do not have the ability, time or expertise, to satisfy the technical requirements of the standard. Fortunately, merchants can rely on service providers to touch, handle and store credit card numbers, which will keep cardholder data away from your organization. There is a simplified version of the PCI self-assessment questionnaire that you can qualify for if you rely on PCI complaint 3^rd parties for the collection and storage of all cardholder data. This allows you to complete the simplified version of the PCI self-assessment questionnaire, by basically stating that you rely on 3rd party providers to handle the cardholder data for your business and that they are PCI compliant. Said more plainly, you are outsourcing the headaches.

This is sometimes referred to as credit card tokenization. If you want to store credit card numbers, but you don’t want the sensitive information to be poking around your server environment, you can give the sensitive bits to your service provider. They will store the card number and give you a “token” in its place. For example, if you gave a credit card number to your provider, they might return a response to you that says “we’ll call this token #50″. Any time you want to bill this card again in the future, you just tell them to bill token #50. This all happens at a technical / API level behind the scenes.

CRM Dynamics employs this tokenization method in our payment pathways solutions. If you’d like to learn more about how we can assist you in keeping your card holder’s data safe, give our experts a shout.

About the Author

David Goodale is CEO of Merchant-Accounts.ca, and is one of Canada’s leading experts in the field of e-commerce payment processing. Over the past 20 years, David has worked with thousands of merchants across Canada, the USA and throughout Europe. David consults for large and complex e-commerce businesses on issues such as cross border payments, interchange optimization, and particularly on approval for hard-to-approve businesses such as airlines, travel businesses, crypto payments, and also for unique and interesting start-ups.

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CRM Software Blog | Dynamics 365

UK Card Delinquency Roll Rates Creep Higher

March 3, 2019 FICO

Balance roll rates — overdue balances on UK cards that go from one 30-day cycle to the next without repayment — moved slightly higher in 2018. Our analysis of data from 11 card issuers across the UK shows the areas that warrant attention from card issuers.

Average current to 1 cycle balance roll rates were at their highest in 2007, with 1 to 2 cycles and 2 to 3 cycles highest in 2010. Whilst 2018 values are noticeably lower than the peak, there has been growth from 2017, ranging from £2,039 to £2,817 dependent on the cycle.

The good news is that, with limits on cards higher now than in 2007 or 2010, the rolled balances are lower than in those years. The bad news is the steady creep up since 2012. This suggest that issuers should review their collections processes and strategies to determine if the focus is still appropriate, based on the current customer performance.

UK Cards Roll Rates UK Card Delinquency Roll Rates Creep Higher

Source: FICO Blog

From Current to 1 Cycles

The percent of accounts and balances rolling from current to 1 cycle is at its lowest in the 8 years included. At their peak in 2006, 5.2% of accounts and 7.8% of balances rolled into 1 cycle. This has dropped to 1% and 2.1%, remaining stable compared to 2017.

What caused the drop? In part, issuers having access to a richer data source over the years, including more customer-level and bureau information, as well as the implementation of pre-delinquency campaigns. Issuers may be focusing on higher-risk segments in order accounts to try to reduce IFRS 9 provisioning levels. The more mature consumer subpopulations may be more cautious in their spending habits, with the last economic downturn still in their minds. It will be interesting to review the rates later in 2019 to gauge the impact of increasing minimum and general payments due to the introduction of the Persistent Debt regulations in September 2018.

From 1 to 2 Cycles

Accounts rolling from 1 to 2 cycles both peaked in 2006, but were also high last year. The November 2018 roll rates — showing 15.5% of cycle 1 accounts and 19.2% of cycle 1 balances rolling — were only exceeded in 2006 and 2009. The percentage of rolling balances are on a par with the 2009 value and if the rise continues will soon reach the 2006 levels.

Further analysis of the accounts rolling will determine help issuers determine which accounts to focus on in collections. Data-driven strategies are becoming more common in the Industry to determine the most effective data to segment based on bad rate. Taking this one step further and optimising the best next treatment will provide additional uplift.

From 2 to 3 Cycles

The highest percentage of accounts rolling from 2 to 3 cycles was reported in 2017, and in 2018 it fell to below the 2009 levels. The percentage of accounts rolling was 51.9% last year, and this represented 57.8% of balances, marginally down on last year, with the highest levels in 2009 (59.6%).

Roll-Back Rates

When reviewing roll-back rates, it is worth noting that individual issuer policies on reage and payment plans can influence results. However, over the years the reage policies have been tightened and consumers have to demonstrate the ability to maintain certain payment levels before their delinquency level is reduced. Regarding payment plans, as there is a mixture of approaches in the Industry, including allowing accounts to roll back or flow through delinquency, and this has not influenced the results.

The most significant year-on-year change for the 3 cycle roll-back results was for the percentage of balances curing. In 2017 the rate was 10% and in 2018 5.7%. However, the average balance curing increased by £1,597 to £5,156. This indicates for many issuers that there is a high proportion of smaller balances that could be targeted.

Industry 2 to 1 cycle rates remained stable since 2017, with just under 10% of accounts rolling. The largest difference was seen for balances and accounts curing, with balances curing decreasing from 26.3% in 2017 to 15.8% last year. Average rolled balances have remained stable.

The percentage of accounts and balances curing from 1 cycle has also reduced annually from over 71% to 61%. Average balances have marginally increased.

Further analysis by issuers could determine why these drops have occurred and may suggest strategic changes to address it.

Our Risk Benchmarking results from December show a worsening in the delinquency results in the market. This indicates that there may be a general downturn in performance, which could also be a contributing factor to roll rate results. Watch for my next posts on this topic and on static delinquency.

To learn more about our cards benchmarking service, or FICO’s Risk Benchmarking Service which forms part of the Fair Isaac Advisors P&L Insight Suite, please contact me at staceywest@fico.com.

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Video: Metrobank Card Deploys Intelligent Collections

July 24, 2018 FICO

Metrobank Card Corporation (MCC), one of the largest payment solution providers in the Philippines, has adopted FICO® Customer Communication Services (CCS) to automate its collections and achieve scale as its business grew rapidly. Using intelligent, analytically-driven communications, Metrobank Card has improved its customer service and automated its early repetitive reminders to free collections staff to work on more complex, strategic work.

“Metrobank Card Corporation was growing faster than the industry average and we needed a solution that could maintain the high standards that we had in collections while providing us with the scalability that we needed,” said Roxy Castro, head of credit operations, Metrobank Card Corporation. “With CCS, we were able to augment our collections capacity rapidly and address the tremendous growth of our bank’s card business. CCS has allowed us scalability for the early part of the collection buckets, where dialogues were simple and repetitive and automation took care of that. On the other hand, for dialogues that were more complicated, we left them to our highly trained collections agents.”

Prior to implementing FICO’s cloud-based solution, Metrobank Card was using customer communications in a very siloed manner. Messages that were sent using voice, SMS or email were not connected or informing a single view on a customer account. They were usually simply payment reminders that did not allow for resolution via a two-way customer response. This meant that agents would have to start a new interaction with the customer if they chose to shift from one communication channel to another.

Now, the payments company is not only able to track communication across channels but has freed up a vast number of hours in its collections team. Automation of early stage collections has fundamentally changed the way the business can resource its team. Metrobank Card was able to scale using the team it already had, avoiding the need to quickly hire dozens of extra collections staff and the associated costs and training headaches.

Read the full release and watch the video below.

[embedded content]

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Debit Card Skimming and Fraud

May 20, 2018 TIBCO Spotfire

This March, in Indonesia we had a hard wake-up call: debit card skimmers are still at large. We found out that more than 1,400 cards were being skimmed from a single team of perpetrators. And 1,200 of those skimmed cards came from a single bank. According to the news, these fraudsters, which started less than a year ago in July 2017, are working around several big cities in Indonesia.

The media reported that the skimmers stole up to 22 Million IDR, which is around $ 1,500 USD (rate USD 1 = IDR 13700), per customer. If we extrapolate the numbers, assuming a customer loses $ 1,000 USD on average (and the bank has to pay that balance back), and around 1,000 customers are impacted each year, that’s approximately $ 1 Million USD that they stole in just a few months by a single team of perps. Also, it’s quite likely that were more incidents than reported, but the banks did not share the actual number so as not to alarm the public.

From the bank’s point of view, while the monetary loss was not monumental (What’s $ 1 million to the billions the bank makes a year?), from the overall business point of view, this will impact:

Trust
Customer experience
Brand Name of the Bank
Being famous for the wrong reasons as an “easy target”, more will try to find ‘loopholes’

A poor perception of trust, customer experience, brand name and getting a reputation of being an easy target, can have a long-lasting negative impact to the bank. To combat this, banks need to implement adaptable anti-fraud solutions as soon as possible that can help them prevent and reduce the activity of these tricky fraudsters.

Based on my experience as a technologist, I have looked at these acts of fraud from the tech angle to see how they might be prevented.

How to prevent bank fraud using TIBCO solutions

First, to understand fraud/skimming, you need to understand how it happens and when it happens. It all depends on data. While most of your data is probably in a data-warehouse, there might be another data source that you use. You might use data from excel, another database or table, a web-service, or even from another SaaS tool such as Google Analytics.

If you have so many disparate sources of data, you can start the fraud detection process by using TIBCO Data Virtualization. Data virtualization gives you a seamless way to collect data from many disparate sources and make all your data uniform so you can a 360-degree view in a single dashboard. After all, you know what they say, “garbage in – garbage out”. You need to have good data going into your dashboard so you get good data coming out of it.

1 Debit Card Skimming and Fraud

Then, using analytical tools such as TIBCO Spotfire combined with your historical data, you can start to analyze your data to find which kind of transaction is likely to be fraudulent or not.

After doing the analysis, the next step would be implementing the analytical model in real-time, either as an event rule or as a streaming analytical event. Both can be used separately or in conjunction with one another. Of course, we’re not stopping at that.

2 Debit Card Skimming and Fraud

While we can monitor and score the transaction in real time, we also need to put in Case Management whenever we find potential fraud. This enables us to catch suspicious activities and also ensures that we don’t block the card unless we’re 100% sure that this is a fraudulent transaction.

The Financial Fraud Accelerator is available from TIBCO Community to help you speed up your time to understanding the data, visualizing the data, building the analytics model, doing machine learning, model scoring, and so on. This accelerator or template is an all-in-one, end-to-end solution for fraud detection.

3 Debit Card Skimming and Fraud

TIBCO’s fraud detection tools can be implemented either in a modular manner or as an end-to-end solution, depending on your needs. So, what are you waiting for? Fraud can be lethal to banks.

Debit Card Skimming Debit Card Skimming and Fraud

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5 Tips for Avoiding Credit Card Debt Problems

March 20, 2018 FICO

In our proprietary P&L Insight Benchmark Reporting Service, FICO has seen some worrying signs for UK credit card accounts 1-5 years on book; these accounts are showing high lines, high spend and increasing delinquency.

The potentially worrying delinquency results come at a time when the FCA aims to try and address the issue of persistent credit card debt, putting requirements in place for banks later in 2018. Others have suggested capping the level of interest that a bankcard issuer can charge.

Consumers who are concerned about their level of credit card debt can use a number of options and tactics to keep their credit healthy.

First, avoid opening credit that you don’t actually need. Credit can be tempting and people can become over-burdened if not careful.
Once credit is open and balances are sitting on the card, have a target for paying off the debt within a set time period, and budget accordingly. That typically means paying more than the monthly minimum payment. Savvy consumers will shop for the credit products that best meet their financial needs. There are hybrid products on the market which offer an APR ranging from 5.7% to 11.1% (compared to standard card interest rates of 18% to 21%) and which bridge the gap between a credit card and a loan.
Be mindful of what you can afford when utilising existing credit. You can and should reject credit line increases if you think the higher amounts could lead to uncomfortable levels of credit card debt, or know you cannot afford the extra spend that this would enable.
Avoid cash withdrawals except in emergencies.
Finally, consumers have the best understanding of their financial situation. As soon as you become aware of impending financial difficulties or changes in circumstance, contact your card issuer and other creditors to discuss your options. This gives both the creditor and consumer more time to make adjustments, and allows a wider range of adjustments to occur, especially if the actions can be taken before an account goes over its credit limit or a payment is missed. Issuers can work with consumers to negotiate payment plans, discuss moving the customer onto a lower rate credit product and review the card limit to ensure it suits the customers’ immediate situation — these are just some of the options available. Engaging earlier and more frequently with creditors can alleviate worry, and prevent a downward spiral of credit card debt that can destroy the borrower’s credit rating.

Above all, consumers should pay at least the minimum due every month, and more if they can afford it, and should not panic if they hit a temporary setback. Banks will work with consumers ahead of impending setbacks that can trigger missed payments and credit card debt problems.

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FICO Data: Warning Signs for New Card Accounts in Ireland

January 31, 2018 FICO

Our analysis of UK and Ireland card trends in the FICO^® Benchmark Reporting Service has revealed some worrying trends for cards issued in Ireland. Over the past few months, the percentage of new Irish card accounts that are delinquent has climbed to around 10%, more than twice the average for UK cards.

We see a similar trend with delinquent balances on new Irish cards, where 13.5% of balances were delinquent in December, compared with 3.5% for UK cards. New accounts are those on book less than a year.

This indicates a riskier population are being accepted in Ireland, and it is worth Irish issuers identifying the reasons for this, as changes to originations policies may be needed. The trend is reinforced by the recent sharp rise in average credit lines for new accounts in the Irish market, allowing potentially higher delinquent balances to flow through.

Average delinquency balances on all cards are more in line with the UK, despite credit limits being approximately 18% lower than the UK, and the more mature accounts are influencing this. For new accounts, though, average 2-cycle delinquent balances are more than 36% higher than in the UK.

Other Trends in Irish Cards vs. UK Cards

More accounts use cash in Ireland (12.4% vs. 6.2%) and the most noticeable difference is for new cards (27.7% vs. 12%). The proportion of cash sales to total sales is also higher, again more noticeably for new accounts, with average total sales (combination of cash and merchandise) higher in Ireland.
Not surprisingly, looking at the delinquency rates, a higher proportion of accounts are not paying the full amount due in Ireland. However, the highest proportion of accounts in both markets pays the full balance off each month.
There is a higher proportion of accounts with a direct debit in the UK. Lower rates in Ireland are influenced by accounts >1 year on book, so there are opportunities here to promote direct debit usage as well.
Average credit lines for accounts >1 year on book Ireland are lower than in the UK. This is influenced by the difference in the regulations, as limits can only be increased at the request of a cardholder in Ireland. However, average credit lines on new Irish accounts have moved noticeably above the UK average in July 2017 and have remained at this level since.

Given these trends, it is not surprising to see a higher overall percentage of overlimit accounts in Ireland. Despite the higher average lines for new accounts in Ireland vs. the UK, the percentage of overlimit accounts also exceeds the UK average. The average amount overlimit in Ireland for accounts <5 years on book is significantly higher than in the UK. Collections teams could review to determine if specific action is required on this subpopulation.

FICO’s Benchmarking Services

The card performance figures are part of the data shared with subscribers of the FICO^® Benchmark Reporting Service, which compares overall market performance in the UK cards market with individual card issuers’ performance. The data sample comes from client reports generated by the FICO^® TRIAD^® Customer Manager solution in use by most UK and Irish card issuers. For more information on the new service, please contact me at staceywest@fico.com.

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Tag Archives: Card

Google releases Model Card Toolkit to promote AI model transparency

ProBeat: Alphabet’s Sidewalk Labs won’t be the last to play the coronavirus card

SPDB Credit Card Centre Automates Early Collections

UBS Card Center Wins Security Innovation Award with FICO AI

Recurring Payments: Storing Card Holder’s Data Safely

UK Card Delinquency Roll Rates Creep Higher

From Current to 1 Cycles

From 1 to 2 Cycles

From 2 to 3 Cycles

Roll-Back Rates

Video: Metrobank Card Deploys Intelligent Collections

Debit Card Skimming and Fraud

5 Tips for Avoiding Credit Card Debt Problems

FICO Data: Warning Signs for New Card Accounts in Ireland

Other Trends in Irish Cards vs. UK Cards

FICO’s Benchmarking Services

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