Category Archives: Predictive Analytics

Digitalist Flash Briefing: Artificial Intelligence: From Novelty To Practical Workplace Application

Digitalist Flash Briefing: Artificial Intelligence: From Novelty To Practical Workplace Application

Bonnie D. Graham

Today’s briefing looks at how businesses have pondered what an intelligent workplace powered by artificial intelligence (AI) might look like. The day is finally here, and it’s not as daunting or as intimidating as many might have anticipated.


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Read more on today’s topic


About Bonnie D. Graham

Bonnie D. Graham is the creator, producer, and host/moderator of Game-Changers Radio series presented by SAP, bringing technology and business strategy discussions to a global audience. Listen to the series flagship, Coffee Break with Game-Changers.

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Authorised Push Payment Fraud – The Liability Challenge

Push Payment Fraud Authorised Push Payment Fraud – The Liability Challenge

Last week, the National Board for Customer Disputes in Sweden, after reviewing cases referred to them, have ruled that banks should be liable for so-called “push payment” fraud losses over a certain amount.

Authorised push payment fraud, or APP fraud, is gaining in popularity in the criminal community. Customers are being tricked into authorising payments by persuasive social engineering schemes run by criminals. These criminals have been so successful that this kind of fraud even has a nickname: hypnofraud.

Fraudsters have always targeted the weakest link in the process. As systems become more and more secure, the weakest link has become the customers themselves.

The push payment fraud trend has sparked debate at Payment Services Providers (banks and other financial institutions), regulators and consumer bodies about who should foot the bill when these kinds of schemes are successful. In 2016, a super complaint by the UK consumer organization, Which, was filed which called for the PSPs to do more to stop this kind of fraud, and to take greater responsibility for the losses when customers fall for these scams.

The question of liability isn’t straightforward, as my colleague Sarah Rutherford noted in a recent post. On one hand, customers are being tricked by highly convincing, almost hypnotic fraudsters, often posing as representatives from a bank. Whilst the industry can educate consumers about this, we can’t expect all customers to be experts in identifying whether calls, emails or SMS are genuine or fraudulent. On the other hand, if a customer withdrew cash from an ATM and was persuaded to hand over that cash by a fraudster, no one would expect the bank to foot the bill.

Whilst regulators and consumer bodies around the world make their own judgements, there is something the banks can do to reduce the scale of this problem and make social engineering scams less successful. By analysing the way each customer normally uses their account — whether transactions are authenticated by them or not — they can detect transactions that are out of character and stop them before funds disappear from accounts.

Customer behaviour profiling is a key way to detect and stop fraud from taking place, whilst allowing a frictionless experience for customers going about their daily business. For more on this, see our posts on the FICO Blog: http://www.fico.com/en/blogs/tag/fraud/.

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STEM: Breaking Down Gender Stereotypes

 STEM: Breaking Down Gender Stereotypes

In a recent survey, people were asked to name female tech leaders. Many said “Alexa” and “Siri.”

Alarming, isn’t it? When LivePerson asked a representative sample of 1,000 American consumers to name a female technology leader, 91.7% of respondents weren’t able to think of any. Of the remaining 8.3%, only 4% actually could name one, and a quarter of those cited Siri or Alexa.

When we break down the numbers, this represents only about 10 people in the survey group. But that’s 10 people out of 1000 for whom the most famous woman in tech is a virtual assistant. How many more people could that be when you expand the sample size?

Meanwhile, more than half of the respondents were able to correctly identify a male leader in tech, with Bill Gates, Elon Musk, and Mark Zuckerberg topping that list. Not only do these results highlight a lack of high-profile women in tech leadership roles, but it also reflects the tech industry’s persistent problem with gender inequality. While there are many reasons and arguments as to why STEM fields are male-dominated, the underrepresentation of women in STEM roles is a real problem, with only 24% of jobs in STEM fields held by women, according to the U.S. Department of Commerce.

Arguments such as “women wouldn’t be interested in science or tech jobs anyway,” and “if they really wanted to work in STEM roles, they would” (yes, these are actual arguments that I have heard people say) are narrow-minded and miss the point.

One factor is the general perception that many people have of STEM fields, gleaned largely from media portrayals. A survey of films made between 1931 and 1984 showed that most portrayed scientists as villains (fewer than 1% portrayed them as the hero). Since then, teenagers interested in STEM have often been portrayed as nerdy social outcasts, ridiculed by the “cooler” kids at school. In a phenomenon often referred to as an “accidental curriculum,” people do learn from film and television, whether or not they are aware of it.

If you asked people to close their eyes and describe what they picture when they think of a scientist, an engineer, a programmer, or even a physics professor, most would probably describe a male. In fact, since 1983, repeated studies have shown that when children are asked to draw a scientist, they overwhelmingly draw old white men. Children usually cited film or cartoon characters as their main source of inspiration, and in the original research, children drew these stereotypical characteristics more and more frequently as they grew older.

Fortunately, this often-misguided perception of STEM professionals is changing for the better. One study found that adults in 2001 were much less likely to hold negative stereotypes about scientists than they were in 1983. They were also more likely to consider a STEM career a good choice for their children or themselves.

This is also starting to improve in the film and television industry also. For example, the popular Marvel movie franchise has not only sought to provide more scientifically accurate references by consulting with actual scientists, but also the films also promote a more diverse culture in an effort to change the perception of the STEM field.

For example, the original “Thor” comic had Natalie Portman’s character, Jane Foster, portrayed as a nurse. The writers and physicists consulted for the Marvel Cinematic Universe version thought it would make more sense if her character was actually a physicist who was studying the wormhole that brought Thor to Earth. It’s a good place to start breaking down gender stereotypes, along with cultural, ethnic, and societal ones.

Plenty of evidence shows that organizations and industries with a more diverse workforce enjoy better reputations, but they also see advantages such as increased profitability, greater innovation, and a broader talent pool. In fact, some research also suggests that many consumers would trust big tech companies to be more ethical if women were at the helm.

While the gender gap is slowly closing, STEM industries have a long way to go to create an environment that welcomes all types of workers.

For more on women in technology, see Women In Tech: Taking On The Gender Divide On Their Terms.

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GDPR For Dummies: An HR Perspective

 GDPR For Dummies: An HR Perspective

Cookies (not the edible kind), privacy statements, privacy policies, opt-in, opt-out…chances are if you’re reading this online, you’ve already had to opt-into something today. Then there are the numerous unread emails from no-reply@abc.com clogging your inbox. The culprit? GDPR.

So what’s it all about—and why, as an HR professional, should you care?

What is it?

The General Data Protection Regulation (GDPR, or EU Regulation 2016/679) came into effect on May 25, 2018, and has been described as a landmark moment in data protection. More than three years in the making, the legislation governs the management of personal data, creating consistent data protection rules across the 28 European Union (EU) member states. The regulation has replaced the Directive 95/46/EC, which has been the basis of European data protection law since its introduction in 1995. The GDPR explicitly defines what it means by the term “personal data:” Any data that identifies or can be used to identify an individual. It also updates the definition of personal data to include technology advances such as IP address, location, and biometric data, for example.

What has driven it?

As noted by EY, the demise of Safe Harbor in 2015 and an increased number of high-profile data breaches in the media have caused concern amongst regulators and consumers as to how personal data was managed, and these were drivers for the regulation.

Why is it so revolutionary?

The regulation introduces new rights for individuals to control and protect their personal data, including:

The right to be forgotten – the right to ask data controllers to erase all personal data without undue delay in certain circumstances.

The right to portability – where individuals have provided personal data to a service provider, they can require the provider to “port” the data to another provider, providing this is technically feasible.

The right to object to profiling – the right not to be subject to a decision based solely on automated processing.

It also introduces a mandatory breach notification, which requires organizations to notify supervisory authority of data breaches without undue delay or within 72 hours, unless the breach is unlikely to pose a risk to individuals. If there is a risk, these individuals must be informed. Perhaps most importantly, there is the cost of non-compliance, which can be extraordinarily high – with fines as much as 4% of a business’ global revenue, or €20,000,000, whichever is higher.

Who does it apply to?

While it is European legislation and applies directly to companies operating from an establishment within the EU member states, it also applies to any location where processing is conducted. Therefore, it impacts any company that is a data processor or data controller of personal data based in the EU, as well as global companies that process personal data about individuals in the EU.

Who enforces it?

Supervisory authorities (SAs): Each member state of the EU will appoint an SA who will work with other member state SAs; the European Data Protection Board will coordinate the SAs. They can conduct audits, review certifications, issue warnings, order a processor or controller to comply with GDPR, impose limitations and even bans on processing, and impose administrative fines.

Now that you have the low-down, the first step is knowing if GDPR is applicable to your organization. If it is, your legal counsel has no doubt already made you well aware of this. As suggested in an earlier GDPR blog, the different activities involved to enable compliance with the GDPR and manage data privacy and protection must be brought together in a coherent and integrated set around the “four pillars” (privacy governance, data management, data security, and consent management), with solutions that deliver the capabilities needed to support each of them and so establish strong governance with best-of-breed technology. 

In summary, GDPR is all about the protection of personal data. Because HR is all about personal data, your solution for storing personal data must be capable of supporting GDPR compliance as your first line of defense. EY also suggests that ‘Privacy by Design’ is a key consideration for GDPR readiness—that is, designing data protection into the development of business processes and new systems. Privacy by design and default is also critical to continued GDPR compliance.

For more insight into data security, see Establish Trust In The Digital Age.

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Russian Credit Health Keeps Rising

Russian Credit Health April 2018 Russian Credit Health Keeps Rising

The two-year trend of improving Russian credit health continued in Q1 2018. Following a slide that lasted four and a half years, the FICO® Credit Health Index for Russia began climbing in April 2016 and climbed another 2 points last quarter, from 92 to 94.

What does this mean? Just as millions of Americans check their FICO Scores to see how their credit is doing, FICO and the National Bureau of Credit Histories (NBKI), Russia’s leading credit bureau, keep tabs on the health of Russian consumers. The FICO Credit Health Index measures Russian credit health, based on the percentage of consumer loans and credit cards reported to NBKI that are delinquent by more than 60 days.

The base was set at 100 in July 2009, and it climbed until the end of October 2011. Then came the long fall, which was arrested in early 2016.

What happened? “The biggest impact to the index came as Russian lenders and their customers embraced a new kind of credit product: the credit card,” said my colleague Eugene Shtemanetyan, who manages FICO’s operations in Russia. “Unfortunately, late payments here didn’t carry the stigma or penalties of late payments on secured credit, such as a mortgage or car loan. It took awhile for the Russian market to understand the importance of timely card payments, and for Russian lenders to adjust their customer risk management practices. The market stabilized, and loans issued in the last three years are higher-quality than the ones from the years prior.”

Risk management is still very much a priority for Russian credit grantors. “The main risks for delinquency remain the same — a decrease in real incomes,” said Alexander Vikulin, CEO of NBKI. “Therefore, lenders need to continue to closely monitor market indicators such as the PTI (payment to income), as well as to monitor the financial behavior of borrowers for all types of loans.”

FICO and NBKI provide Russian creditors with data to help them better understand how the credit market is developing and to build quality loan portfolios. FICO Scores, available through NBKI, are used by more than half of the leading Russian banks.

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Improving Customer Empathy With Machine Learning

 Improving Customer Empathy With Machine Learning

In a February 2018 interview, Liz Goli, Commissioner of Queensland’s Office of State Revenue (OSR), sat back in her chair: “The machine can actually improve our empathy with our customers,” she reflected. Now that’s interesting – the idea that an unfeeling machine could help human beings be more empathetic towards other human beings!

Late last year, OSR implemented a successful machine learning prototype, and it’s moving forward with a production pilot of this emerging technology. “We don’t want a system where the machine is making decisions. But we do want the machine to offer up next best-action recommendations to our staff that they have the option to follow – or not – based on their experience and knowledge of how the legislation should be applied… We’d also like a system that can ingest Big Data and take action within certain parameters. For example, in case of a natural disaster, the machine might be able to find out which customers are impacted and replace debt-collection notices with proactive letters giving additional time to pay.”

From action-reaction to proactive and personalized

OSR is responsible for collecting taxes and royalties and administering the First Home Owners’ Grant for Australians residing in the State of Queensland. The revenue collected by the Office provides about A$ 17 billion (€11 billion) in annual income for the state, which is reinvested in roads, schools, hospitals, and community services. With less than 500 staff members servicing over 2 million taxpayers, the Office needs to deliver highly efficient and automated services while minimizing costly and time-consuming manual processes. Moreover, OSR needs to unlock the information in the Office’s vast data holdings to deliver the kind of customer-centric, digitally enabled services desired by government, businesses, and the community.

Timely collection of taxation revenue is key to the government’s ability to fund essential services. But each year up to five percent of revenues are uncollected by the due date, amounting to an A$ 882 million (€555 million) liability in FY16. Default on Land Tax is particularly high, with over 15% of revenues uncollected by the due date, amounting to an A$ 112 million (€70 million) liability in FY16.

For most of us, it’s difficult to fathom how such sums of money could be recovered, but as Ms. Goli said, what happens at an individual level is actually quite simple: “We expect the debtor to pay, and if they don’t then we start to remind them, and after each reminder we sit back and wait. Our process can be described as action-reaction, action-reaction – every action we do is supposed to prompt a reaction from the customer. But because we haven’t historically done a lot of analysis about what reactions our actions provoked, we haven’t always understood our customers’ motivations.”

Therefore, to achieve their strategic objective of reducing liabilities, Ms. Goli and her team knew that they first needed to understand what factors lead some customers to pay on time, while others do not. The challenge was how to uncover the insights buried within the Office’s Big Data holdings – this is where machine learning came in. The Office’s machine learning prototype analyzed 187 million records to provide a prediction of risk by taxpayer and identify the events and influences that lead to payment default. These may be things that OSR has control over (e.g., processes and interactions), things that the government dictates (e.g., policy and legislation), or external impacts (e.g., natural disasters). The machine makes the links between cause and effect, enabling the Office to be proactive in its responses and personalized in its treatment.

Understanding customer motivations

But at the individual level, how do you begin to understand the motivations of someone you’ve never even met? The answer lies in visualization of their journey. Ms. Goli explains: “Traditionally we’ve worked with data in spreadsheets, but we’ve discovered that data visualization is really important. People are visual, and we’re better able to identify patterns with a visual representation of data than with data in a spreadsheet.” So, it’s not only the surfacing of key events and influences, but also how these are presented on a timeline that enables staff to truly understand customer motivations.

In the example of one high-value taxpayer, OSR discovered that his behavior over five years has been to ignore the Office’s debt collection notices until he receives a final legal notice, at which point he promptly settles his debt. Visualization of this particular customer’s journey caused OSR to conclude that his behavior is not motivated by an inability to pay on time, but by a deliberate tactic of delayed settlement. Now the Office has the insight required to design a debt collection strategy for this cohort of one. “We can write to him explaining that we’ve noticed that he only ever pays on the final notice, so we’re not going to bother him anymore with multiple reminders – from now on he’ll get one reminder, then the next letter will be a final legal notice. Equally, for taxpayers who typically do the right thing but are non-compliant in a particular instance, we can design a strategy for them.”

A right-from-the-start approach

Ultimately, the Office’s debt collection strategy is all about proactive compliance. In this respect, OSR has borrowed the mantra “right from the start” from the OECD’s Forum on Tax Administration (FTA). The FTA aims to influence the environment in which tax systems operate to move from a confrontational dialogue to more constructive engagement with taxpayers. The proactive compliance approach recognizes that taxpayers are motivated by perceptions of deterrence (the risk of detection and the severity of punishment), norms (both personal and social), opportunities for non-compliance, fairness (distributive, procedural, and retributive), economic factors, and interactions between the taxpayer and the revenue office.

Right from the start” emphasizes the need to create an environment that encourages compliant behavior by acting in real time and up-front; focusing on end-to-end processes; making it easy to comply (and difficult not to); and actively involving and engaging taxpayers to achieve a better understanding of their perspective. For OSR, this translates into four policy and practice strategies: designing risk-based revenue management interactions; fostering meaningful relationships with customers and partners; developing enhanced services through digital methods; and building a capable, change-responsive workforce. Perhaps most importantly, the Office is leveraging the insights gained through machine learning to redesign business processes with the customer at the center.

Customer-centricity is about efficiency and confidence

It’s not uncommon to for retail expectations of customer self-service to be transposed onto digital government initiatives. But as Ms. Goli explains, “in a government context, customer-centricity isn’t about providing a retail-like online shopping experience. It’s about providing a highly efficient service where people have confidence that they’re receiving the right information at the right time.” To achieve this, OSR needs to leverage its Big Data assets and apply them in a transparent way. “We want to get to the point that what we see is what they see. We’ll show them what we know about them, and they can correct it with us. This will create a mature relationship built on mutual obligations, where we trust them and they trust us.”

But customer-centricity isn’t just about delivering a great customer experience – it also has a role to play in delivering the Office’s proactive compliance objectives. Where traditionally revenue offices tend to look at compliance tax-by-tax, a customer-centric approach checks whether the taxpayer is fully compliant across all their tax affairs. For example, some businesses might always ensure that they’re compliant for one type of tax, where the consequences for non-payment are greater, but they’re consistently non-compliant for other types of taxes. In this way, a customer-centric view gives insight into the taxpayer’s true compliance behavior. This might cause the Office to reassess whether it should continue to offer payment arrangements for one tax type to someone who is a serial late-payer of other taxes, or whether it should take the standard approach with someone who is generally compliant across all their payment obligations.

Further, since debt is often a leading indicator of hardship, a customer-centric approach can highlight instances where a taxpayer might be struggling or a business might be failing. This could prompt the government to proactively reach out to the customer with an offer of assistance. When asked to summarize how she expects machine learning to change the way the Office engages with taxpayers in the future, Ms. Goli replied, “in the midst of all the digital, people want a human connection more than ever before. A connection that is proactive and personalized. Machine learning will provide OSR with a capability to deliver this to our customers, completely transforming our engagement in the future.”

Enabling data-driven policy and practice

By enabling evidence-based decision-making, machine learning is fundamentally changing the ways of working at OSR. Ms. Goli and her team see the potential for:

  • Manual decision-making based on only a small percentage of the data that is available, to be replaced by machine-generated proposals based on all available data;
  • Revenue agents to be able to leverage the insights garnered from machine learning while on calls with customers to better understand their situation and provide enhanced levels of service;
  • Collection agents to be freed from actions that drive little value to focus on interventions that will make a real difference to both the Office’s customers and revenue outcomes; and
  • Risk profiling and segmentation to be used to drive more proactive campaigns and compliance activities aligning with the Office’s risk-based revenue management approach.

The insights gained through machine learning also have the potential to be used as input into future policy development. “We now have the evidence to support our advice that if you design it this way, this is what the likely reaction will be.” Thereby, data-driven insights can help strengthen the voice of the administrative arm of government to policy-makers, influencing legislative change based on service delivery experience.

Gather more insight on The Human Side Of Machine Learning.

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Top Ten Digitalist Magazine Posts Of The Week [June 11, 2018]

Top Ten Digitalist Magazine Posts Of The Week [June 11, 2018]

Shelly Dutton

300w x 200h 300x200 Top Ten Digitalist Magazine Posts Of The Week [June 11, 2018]Digitalist Magazine, online edition, covers a variety of topics around the challenges businesses face in the digital economy. Whether you’re interested in the future of work, customer experience, digital economy, the Internet of Things, or digital supply networks, we provide a vast array of thought leadership and real-life stories on the practical application of digital technology.

Each week on Digitalist Magazine, we publish a list of the top ten posts of the week from across our content categories. We hope you find these articles valuable, informative, and interesting.

Elephants on the Balance Sheet

Multiplier Effect

Say What?!?

Circular Economy: Reshaping the Industrial Ecosystem

Understanding How Machine Learning And AI Can Positively Impact Your Organization

Batteries Power Up

Stitching Up Your Healthcare Data

Stop Metrics Mania

Wealth and Wellness

4 Ways Professional Services Companies Can Outsmart The Competition


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How IKEA Builds Sustainable Innovation Into Its Business Model To Improve Lives

 How IKEA Builds Sustainable Innovation Into Its Business Model To Improve Lives

Most people think of a discarded plastic bottle as waste. But at IKEA, it’s a resource.

The IKEA brand has a set of commandments for doing business, and not wasting resources is one of them.

For example, every year, about 100 billion plastic water bottles are used worldwide, but only 30 percent are recycled, the rest ending in landfills or polluting the ocean. The furniture giant is committed to proving that recycled plastic can be used in the large-scale production of household goods. One example is a new line of kitchen fronts made of plastic and reclaimed industrial wood. The result is a product line that’s not only durable and beautiful, it’s sustainable.

Thanks to some of IKEA’s other business commandments, such as thinking differently and taking responsibility, the company is showing the world how a circular economy can function at scale in every part of their business.

“We’re even looking into circular solutions for our hardware equipment so it won’t end up in a landfill,” says Kristin Grimsdottir. As sustainability manager at operations & shared services at IKEA Group, she is responsible for a team that runs and enables sustainable IT solutions for IKEA Group.

Democratic design

When asked to describe IKEA’s vision for the future at the recent ThinkX event in Stockholm co-sponsored by SAP and Singularity University, Kristin Grimsdottir responds with passion.

“We are not merely a home furnishing company; we focus on life at home and how we can make it better for people. For instance, we’re already helping customers generate their own energy with home solar panels and battery storage options and exploring the area of urban organic farming so you can grow your own food in your kitchen,” she explains.

It is one of IKEA’s core beliefs that everyone has a right to a better everyday life. IKEA’s business idea is to offer well-designed furniture at an affordable price for the many people. One of the big movements going forward is about becoming even more affordable so that many more people can enjoy a better life at home– without compromising on sustainability, quality, or design.

This is possible thanks to the company’s principles of Democratic design. For every new product, the design team first sets the price and then works from there to create functional, attractive, high-quality items from sustainable materials.

Purpose-driven growth

A circular IKEA that reuses or recycles all materials is one way to prepare for the future, another is to drive efficiency through digitalization.

Clearly, there is no lack of innovation in the company. What’s missing is the seamless experience for customers that is a must in the digital world.

While IKEA is actively rolling out its e-commerce solution, Grimsdottir admits that they still have areas of improvement on the e-commerce front. But IKEA is embracing digital technologies elsewhere too. “For example, we’ve implemented IKEA Place, an augmented reality app that helps you decorate your home virtually”, she says.

For IKEA, continued growth requires the transformation of business and IT to implement a more modern IT landscape, develop advanced analytics capabilities and implement more efficient end to end processes. But more importantly, it also requires full buy-in from employees.

“Change is the new normal,” says Grimsdottir, “so it’s important that all of us try to embrace it. We are not implementing automation technology/AI in order to get rid of people but to streamline processes in order to reduce waste and increase efficiency and precision. It is important to be better to meet our customers’ expectations. And that gives our co-workers the opportunity to grow and develop more human, less robotic skills that are believed to be even more critical in the future.”

Every company has a purpose. For IKEA, it’s about creating a better everyday life for the many people without compromising on price, form, function, quality, or the environment.

After all, as IKEA founder Ingvar Kamprad said, “To design a desk which may cost $ 1,000 is easy for a furniture designer, but to design a functional and good desk which shall cost $ 50 can only be done by the very best.”

This story also appears on SAP Innovation Spotlight.

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Analytics Reveal What New Credit UK Consumers Can Afford

Affordability Risk Analytics Reveal What New Credit UK Consumers Can Afford

In the UK, affordability risk has been the subject of increased scrutiny by the Financial Conduct Authority, which, in consultation with lenders, has begun a process of stricter control in terms of treatment of consumers and assessment of their financial vulnerabilities. The goal has been to stop the rising numbers of British borrowers in a state of persistent debt.

At Money2020 today in Amsterdam, we took an important step toward helping UK lenders crack this puzzle. Along with our partners Equifax, we launched a product to address the combined issue of credit and affordability risk, initially in the UK market.

This product stems from extensive research into affordability risk, summarized in a recent white paper written by my colleague, Dr. Andrew Jennings. It’s a hot issue because consumers have a huge appetite for credit, and more “credit-hungry” consumers will normally present a greater risk to lenders. Still, it has been very difficult for a lender to understand the pressures on any consumer in terms of their ability to absorb more credit and pay off the required instalments, without placing unbearable stress on their finances.

During origination, a lender can ask for evidence of income and expenditure if required, and also use data available from sources such as credit bureaux to try to understand the financial circumstances of the applicant. But what happens after a loan is approved, when, say, a lender wants to increase a customer’s credit line, or cross-sell them a new credit product?

It is infeasible to continually request paperwork from a consumer to continue to assess affordability risk. In many respects, understanding the pressure consumer has in meeting their financial obligations cannot be measured.

The FICO® Risk and Affordability Decision Suite, powered by Equifax, is a result of significant research and development by FICO and Equifax and combines a suite of over 46 curated decision keys, including 5 analytics from both FICO and Equifax.

These include:

  • A new consumer-level FICO® Customer Management Score for risk assessment, developed using a combination of traditional methodologies and machine learning techniques
  • A Balance Change Sensitivity Index that identifies which customers would have a significant change in Probability of Default (PD) if they had a sizeable change in their credit card balance
  • An Indebtedness Score, developed on a consumer-level outcome definition to identify customers that are more likely to fall into arrears due to unsustainable credit commitments and higher debt-to-income levels
  • An Affordability Index, which uses trended information on the level and consistency of the funding of the customer’s principal current account and overdraft utilisation to provide new insight into a customer’s cash-flow and affordability position.

When considering the affordability stress on a consumer, it’s critical to use the most up-to-date data. This solution streamlines the delivery of data from Equifax and presents it to the decision processing system of a lender or processor, on a daily basis just in time for account cycling and decisioning in a fully end-to-end managed solution.

For more information on the research behind this solution, read our white paper, A New Challenge for Risk Management: Understanding Consumer Affordability Risk.

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What Did We Learn in 2018’s Cybersecurity Survey?

Cybersecurity Survey FICO Ovum What Did We Learn in 2018’s Cybersecurity Survey?

In 2017 we commissioned independent research company Ovum to carry out a cybersecurity survey among senior executives, with some surprising results. We and Ovum have just completed an even bigger cybersecurity survey, covering 500 senior executives in 11 countries. Organizations from a range of business were surveyed with sizes ranging in bands from 500 employees up to those with over 10,000 employees.

Through 20 questions, we uncovered some interesting statistics and #cybertrends, including:

  • 60% of businesses are expecting levels of investment in cybersecurity to go up in the coming year – for power and utilities companies it was 70%.
  • 76% of organizations have some level of cyber risk insurance – but only half of them consider it comprehensive cover.
  • When asked how cyber ready their organization is, a massive 95% think they are at least average compared to their competitors – a whopping 39% say they’re ‘top performers’.

Ovum discusses key findings from the cybersecurity survey in the white paper, ‘Cybersecurity Survey: Investments, Insurance and Inflated Confidence’. This considers whether IT and senior management are overstating their ability to deal with cyber attacks.

Read the paper now for more on the key findings that:

  • Organizations are overconfident about their cyber-readiness
  • Cyberthreats are rising and increased spending is the positive industry response.
  • Take up of cyber-risk insurance (CRI) is growing, but comprehensive use and satisfaction rates are low.
  • Pressure to improve cyberthreat protection is increasing from all sources.

I will provide further comment on our findings in future posts.

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