Category Archives: FICO

Gamification in the Auto Industry; Can it Drive Results?

Guest Post: Today we have a guest post about gamification in auto from Yu-kai Chou, Founder and President of The Octalysis Group. He was a featured speaker at our Automotive Mastermind event held last month in Silicon Valley. The Octalysis Group is a gamification consultancy that assists companies to understand the core drivers that motivate people to take action and applies these to business objectives.


Driving Engagement in Experiences is a Necessity In The New World

All around the world in many different industries from software to education, banking to automotive, businesses have been focused on making products that are more efficient and effective but not more engaging and interesting.

To be successful in the new age of engagement, businesses need to be optimized for human motivation. Essentially, successful companies are realising the need to shift from ‘function-focused design’ to ‘human-focused design’.

Human-focused design strives to answer the key question: “Why do people want to engage with the products, services and environments designed for them?”

We believe that games can show us the way here. The gaming industry was one of the early adopters of human focus design. We believe that drawing insights from the game industry can help people design entertaining experiences for ‘professional’ service users, and market consumers.

The game studios behind franchises such as World of Warcraft, Heroes of the Storm, Zelda, Farmville or Chess have spent decades mastering how to engage people with human-focused design. We are now learning from those games to create similar engagement, hence we call it ‘gamification’.

Of course many games are failures. So what makes a game successful? I have published something called the Octalysis. It is named Octalysis because I discovered there were 8 core drives and almost everything we do, every human behaviour, is based on one of these core drives. So in order for all of us to create engagement in industries like automotive, we can use the Octalysis Framework to drive business metrics through behavioural science.

Gamification in Auto – What is the Octalysis Framework?

Octalysis rests on the belief that human motivation is based on eight core drives.

Octalysis 1024x977 Gamification in the Auto Industry; Can it Drive Results?

8 Core Drives

Core Drive 1, Epic Meaning and Calling

This is the core drive which is based on wanting to be a part of something bigger than yourself: you are the hero of the story and have a great quest to fulfil.

A game that harnessed this drive really well was ‘Sea Hero Quest’, which was created to help assist dementia research. You are a sea explorer, recovering memories for your father. While you’re moving around, the researcher receives data in form of a heat map and can then analyse how people are navigating through the ocean maze. “If 100,000 people play for 2 minutes, they generate an equivalent of more than 50 years of similar, lab-based research”. This database can then be used to detect patterns of abnormal navigating behaviours. Loss of spatial navigation ability is one of the first signs of dementia.

In automotive, Tesla and Elon Musk do a good job of this. Tesla drivers want to stand up for their cars. Tesla has created a calling around their cars, in terms of their environmental record and ‘saving the world’. This drive is so strong than when there is bad press, say around the self-driving feature doing something it wasn’t meant to do, Tesla drivers will often say that it was their actions, not the car’s that created the accident.

SeaHero 1024x563 Gamification in the Auto Industry; Can it Drive Results?

Sea Hero Quest – help scientists fight dementia. (2017)

Core Drive 2, Development and Accomplishment

It is this core drive that makes you feel a sense of progress and achievement by overcoming challenges.

A great example of development and accomplishment is Fitbit, which makes life a sport, keeping track of how much you move during the day while giving you a tangible feedback of your progress. The application also provides users with statistics about their calories consumption, water intake, as well as sleeping hours.

Many designers love to create gamification based on points, badges, and leaderboards and think that somehow people automatically get long term engaged by them. In the end, this kind of extrinsic rewards are motivational mainly for the short term and lose a lot of their motivational value for senior users. Moreover, the more you focus on getting people to do things for a reward, the more they often lose their intrinsic (inner) motivation to do an activity.

In automotive, it isn’t points or leaderboards, but rather some people buy expensive cars because they believe it is a status symbol. Consumers are willing to spend 80K more to feel more status. That is the same as virtual goods in a game, where people pay money to look cooler – probably one of the more lucrative ways to introduce gamification in auto.

Core Drive 3, Empowerment of Creativity and Feedback

This is the core drive that awakens the creator in us. It is one of my personal favourites because it creates a lot of autonomy. For example, games such as Lego lets you (re)create anything you can imagine: a castle, your favourite movie character or any life situation.

In automotive, we see this when consumers go to a car company and they encourage you to go on their website and look at all the ways you can customise their cars – done well it can be a very creative and fun process which makes the brand very ‘sticky’ for the buyer.

Core Drive 4, Ownership and Possession

You are motivated because you own something you want to improve, protect and get more of it. Collecting shoes, stamps, Pokémon or even money is motivated by that core drive. If you continue your collection, you feel more and more ownership and you will want to get more and more of it.

In automotive I have seen this with auto insurance sales. One company was having a problem where the salespeople were closing the contracts, but the end-consumers were getting buyer’s remorse by the time the paperwork arrived a week or so later. So they changed one thing. After the salesperson closed the deal they asked the consumer ‘why did you choose to buy from us?’ They would answer that the company provided great value or a great product. What the company found, was that once you can get the consumer to speak about the benefits themselves, they doubled their sales as they had managed to get the consumer to take ownership of their decision.

Core Drive 5, Social Influence and Relatedness

This core drive is motivating you to be inspired by what others think, do or say. It is the core drive that lets you check your messages on Facebook, gives you a sense of belonging if you have a mentor and makes you feel jealous if you see something you don’t have.

In automotive, I remember going to a Porsche dealership and asking the sales person what the Porsche offered me over a Tesla. The response from the dealer was that by buying the Porsche you are entering an esteemed group, the Porsche family. You can say, ‘I am a Porsche person’ and you see this a lot where some families have always owned a particular brand of car.

Core Drive 6, Scarcity and Impatience

This core drive is based on the feeling that when you can’t have something yet, you want it even more. A successful example for that is Kickstarter, an online crowdfunding platform that first dangles with an amazing product, then uses a what we call ‘last mile drive’ to tell you that it just needs, say, $ 1000 more to reach its goal, shows you a timer to urge you to act immediately and tells you that there is a limit of four pledges in that category.

Core Drive 7, Unpredictability and Curiosity

What happens next? How will the story end? What’s hidden behind this door? These are questions you would ask yourself motivated by this core drive. It is the main driver to watch horror movies, play with slot machines or follow a football game.

The New York Public Library created a treasure hunt where 500 selected people spent a night at the museum collecting 100 stories to write a book together and ‘Find the Future’. To make the experience more engaging they didn’t just tell people to write a book, they made them curious about what content they could write about and how they could make history. They created a theme and a storyline around the experience which first needed to be discovered, leading them towards the end goal, to write a book together in one night.

Core Drive 8, Loss and Avoidance

Last, but not least, this drive is based on the fear of losing something you own or avoiding something negative to happen. Think of it as expiry dates on a shopping voucher or your crops withering on Farmville. Also part of this core drive is a very controversial gamification example up to date. China’s government (with the help of companies like the Alibaba Group, Asia’s biggest e-commerce provider) is in the process of building the Sesame Credit System. They are experimenting with big data to monitor behaviour and create a credit score for performance as a citizen. Sesame Credit will give you a score for your behaviour, your credit history as well as your social connections.

In automotive, Volkswagen turned this idea of loss and avoidance on its head. They created gamification in auto by building a speed camera lottery experiment. Installed on one road, the camera would take photos of people who were speeding and they would receive the normal police fine. However to encourage people not to speed the twist came from then giving a cash jackpot to random cars that were travelling below the speed limit.

Extrinsic Motivation vs. Intrinsic Motivation Design

Unlike robots, we are hard-wired to create new things, solve problems and receive feedback. Humans are intrinsically motivated to do this without being given or looking for a specific reward.

Extrinsic motivation happens when we do an activity mainly because of a reward, milestone, or goal – but we may not necessarily enjoy the activity itself. Doing grunt, repetitive work, like moving boxes, is something nobody will do for real without an extrinsic purpose or reward to it. These core drives are situated on the left side of the Octalysis octagon.

Intrinsic motivation is when we do something simply because we enjoy doing it – we would even pay money just to experience it. Even if all “progress” is lost the next day, we may still enjoy spending time with our friends or play a friendly game of basketball. In the Octalysis Octagon, you find these core drives on the right-hand side, indicating what we call the fictive right brain.

Many companies default to extrinsic motivation because it is simpler to design. However, the Over-justification effect shows that, once someone does a behavioural because of extrinsic motivation, they would lose their initial intrinsic motivation of doing that activity. Once the extrinsic goal or reward is no longer there, they would have even less motivation to do something than before (for instance: people who used to go to a restaurant because they enjoyed it, but later on would only go to the restaurant if they received a coupon).

We always have to balance our choice of designs carefully so that we do not overemphasize extrinsic rewards over intrinsic motivation.

Not everybody is motivated in the same way. Some of us are highly competitive, while others like to explore and take their time. Usually, we can optimize a product’s design for one or two player types. It’s like building a company, you can’t assume that everyone will like your product or service but need to focus on a specific customer segment that it is targeted towards. Finding the right combination is both an art and a science.

Who will win the Battle for Engagement?

Clearly, gamification is not a one-size-fits-it-all solution: it is an intricate process of trade-offs to create more motivation, engagement, and conversions within an experience. However, the rewards of gamification are immense. In the new world where every product, phone screen, and advertisement is fighting for our limited attention, people will not just take action on proposals that make logical sense, but on experiences that engage their minds. The customers who are too busy to research and buy your products often have enough time to play games for hours a day. With cars being a passion for so many people, the possibilities for gamification in auto are not only present but there for the taking.

In this new era of human-focused design, if you lose the battle of engagement and fun, you lose the hearts and minds of your customers to other companies that are dedicated to getting it right.

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Smart Government Agencies Turn to Next-Gen Customer Comms

Government agencies are often challenged in their effort to optimize communications with their citizens. Most government executives want to increase the frequency and quality of their interactions, but tightening budgets have resulted in fewer interactions, not more. They’ve stopped sending annual booklets, they send fewer reminder notices and wait for consumers to call them rather than proactively reaching out. Reduced communication results in lower compliance and an increase in downstream problems.

At the same time, more people than ever are using smart phones (SMS, email and web apps) and other electronic communication tools. Recent data from Deloitte tells us that 77 percent of Americans own a smartphone and its usage is so ingrained in our daily routine that 61 percent of Americans check their smartphone within five minutes of waking up.

How long after you wake up do you check your smart phone?

howlongafterwakingphone 1024x743 Smart Government Agencies Turn to Next Gen Customer Comms

With this as a backdrop, it’s no wonder private sector firms are turning to automated communications to increase the quality and quantity of their customer engagement. This is where automated communications can also help government agencies. Not only is automated communication less expensive, it requires less staff to manage, and enhances the citizen experience. Automation improves the sophistication of the contact strategy with each citizen, ensuring they receive messages in their preferred channel, at an optimal time with clear instructions on what to do next. This results in increased compliance and a decrease in inbound phone calls. Customer service ratings also go up as the contact strategy is less intrusive, more personalized and seen as more convenient in our busy modern lives. The opportunities to improve engagement are broad. Let’s look at just one application of automated communication for governments, the collections of taxes:

Example: Tax Collection
Customer Service

  • Reminders before due dates. Businesses and individuals register for automated reminders prior to due dates. Through SMS and email outreach, the taxpayer benefits from reminders, and the department benefits from lower non-filer rates.
  • Account clean-up and closing. Taxpayers who have not filed recently, and have no other record of activities can be contacted to automatically close their account. This cleans up tax rolls and reduces non-filer and compliance activities.
  • Customer Service Campaigns. After new laws or regulations have been enacted, targeted reminders are sent automatically.


  • Refund Status. Many tax agencies are inundated with phone calls from taxpayers asking about the status of their refund. Imagine having a spot on the tax form where the taxpayer can request text messages regarding their refund status. An automated SMS can be sent for ‘return received’, ‘return processed/refund approved’ and ‘refund sent’, for example. Each can include an estimated refund date. This would reduce phone calls and if the taxpayer is delinquent in a subsequent year, documents the phone number to call.
  • Filing Zero Returns. If a taxpayer has signed up for a filing date reminder but still does not file on-time, and if that taxpayer has a history of ‘zero’ returns, send an email or text message with a link to certify a zero return for that period.
  • Registration Renewal. Reminders can be sent for registration renewals and automated renewals could be provided using the same payment source used the prior period.

One of the best opportunities for advanced contact is for taxpayers in collections. While the department would require an “opt in” process to use this method, once approved by the taxpayer, the department can dramatically reduce their cost of collections and increase the breadth of contacts through these methods:

  • Text messages or emails replacing or supplementing US Mail. US Mail contacts are expensive and delay the collection process by a handful of days. Contacts that are not mandated by statute could be replaced or supplemented with emails or text messages, expanding reach and reducing costs.
  • Fully Automated, Unattended Phone Calls. Many agencies use predictive dialers to enhance their outbound call campaigns. Technology now offers fully interactive voice phone calls. These calls allow the taxpayer to make a payment, enter into a payment arrangement, or connect with a live agent. This approach can leave messages which connect call-backs with an automated attendant rather than a live agent. Experience shows most people prefer this to a phone call with a collector (as it is less embarrassing, intrusive and confronting)
  • Smart Phone App for Payments and Payment Agreements. Apps can provide taxpayers with their balance and facilitate payments or enter payment agreements. It can also hold a payment source for recurring payments which can be used as a levy source if needed.
  • Initial contact. While the state likely needs to use US Mail to make official contact with the taxpayer to establish due process, the initial contact could potentially be made much less expensively using email or text messages. This results in significant savings and for low risk taxpayers, can resolve delinquencies faster.

Desk Audit

  • Automated contacts. Audits are typically conducted using US Mail and the telephone. In the future, individuals and businesses can be contacted using SMS, email and smartphone app. This speeds up case resolution and improves customer service.

Enhanced with Predictive Analytics

  • Continually improved operations. Technology can be used to evaluate the results of the programs mentioned here. Predictive analytics and machine learning can determine the optimal number of days before or after a due date to make contact, the relative success rates of SMS, email, smartphone app and live calls to continually improve strategies.

To learn more about upgrading your own communication technologies and services, download FICO® Solutions for Government, email me at or call 1 888 342 6336.

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NCAP Medical Collection Removals are Rare and Have No Material Impact to FICO® Scores

Medical Collections Banner Image NCAP Medical Collection Removals are Rare and Have No Material Impact to FICO® Scores

The National Consumer Assistance Plan (NCAP) is a comprehensive series of initiatives intended to evaluate the accuracy of credit reports, the process of dealing with credit information, and consumer transparency. In a previous post, we showed that July 2017 NCAP public record removals (civil judgments and some tax liens) had no material impact to FICO® Scores. In mid-September 2017, the three consumer reporting agencies (CRAs) are also scheduled to remove the following from credit reports:

  • Medical collections less than 180 days old
  • Medical collections that are ‘paid by insurance’

FICO recently conducted research on a representative sample of millions of US consumers to assess the impact of the NCAP-driven removal of these 3rd party medical collection agency accounts on the FICO® Score.  Our results showed that NCAP-related medical collection removals have no material impact on the aggregate population to the FICO® Score’s predictive performance, odds-to-score relationship, or score distribution.

The removal of medical collections less than 180 days old is based on the date of first delinquency. Since most medical collections aren’t reported to the CRAs until more than 180 days after the first delinquency, we found that only 0.1% of the total FICO scorable population (roughly 200,000 consumers out of ~200 million) has a medical collection less than 180 days old. Medical collections that are identified in the credit file as being ‘paid by insurance’ are even less common.

Of the impacted population, roughly 3 in 4 saw score changes of less than 20 points. Most consumers with these medical collections have other derogatory information on their credit files, resulting in minimal impact to their FICO® Score once these collections are removed.

In 2014, FICO® Score 9 introduced a more sophisticated way of assessing collections, by ignoring all paid collections and differentiating unpaid medical collections from unpaid non-medical collections. Therefore, paid medical collections removed because of NCAP would already have been bypassed from FICO® Score 9.

FICO® Score 9’s enhancements led to improved predictive performance, while ensuring that those with different types of collections would receive a score commensurate with their credit risk. So these enhancements benefited lenders with a more predictive FICO® Score, while benefiting consumers who took a positive step toward credit responsibility and paid off a collection.

In conclusion, since NCAP medical collection removals are so rare, we observed virtually no perceptible impact to the ability of FICO® Scores to rank-order risk, volumes above or below score cut-offs, or bad rates at any given FICO® Score. Lenders can continue to rely on the stability and predictive performance of the industry standard FICO® Score.

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My Top 10 Attributes of a Data Leader

10 Data Leaders Analytics FICO My Top 10 Attributes of a Data Leader

A recent article in Information Age named 10 attributes of data leader. Each of the experts quoted discussed just one attribute — in my case, it was separating hype from potential, which is critical when you’re dealing with advanced analytics. Here’s an excerpt of what I wrote for that article:

“In the Big Data era, businesses have been told to save every scrap of data because it might turn out to be a valuable clue to customer understanding or business performance. While the cost of storing data has fallen dramatically, it doesn’t represent the true cost of data, which includes ensuring quality, maintaining freshness and mitigating the risk of a breach.

“Data leaders are equally discerning about the hype and potential of the analytics used on their data. They focus on the problems the business needs to solve, and investigate new analytic techniques that can make their data “talk” in increasingly valuable ways. They will be exploring artificial intelligence and machine learning, but won’t buy into the hype that machine learning plus Big Data will magically solve all their problems. Anyone can find patterns in data — data leaders do the work to ensure they’re finding meaningful, actionable patterns, not arbitrary correlations.”

I agree with the other attributes listed in the article, but of course I have my own list for what a true data leader needs to do in an advanced analytics environment:

 1. Understand the basics.

The first rule of data is “garbage in = garbage out.” Ensure regular and formal data governance, knowing that there is one source of the data, and that the data must be regularly updated and refreshed. Data quality is #1.

 2. Focus on problem definition.

Find the essential data to solve the business problem, don’t fall into the Big Data hype cycle. Data leaders know that they are solving business problems and need to prescribe the data required to solve the problem, rather than hope that some Big Data Exercise will help find nuggets of greatness. (As I noted in the Information Age article, this will be even more important under Europe’s GDPR regulation.)

 3. Ensure that data is properly usable in terms of business contracts and client consent for use.

Don’t misuse or lose trust – use data for prescribed contractual allowed uses.

 4. Understand coverage.

This means knowing where data is well represented and models can be developed, and where data anomalies can crop up or where assumptions based on data may fall down.

 5. Maintain data as a valuable asset.

Ensure clean, well-controlled, and permissible use. Data leaders understand that the best algorithm is constrained by the quality of the data; they keep their heads down and they ensure they treat data as a tremendous asset to the business.

 6. Don’t be fooled by simple correlations.

Instead, look for causation and demonstrate relationships around what you’re trying to understand. The pursuit of highest-quality data standards, quality and governance differentiates companies that will build industry-leading analytics.

 7. Monitor the data for anomalies and statistical variations.

Data leaders use technology (such as the auto-encoders I have discussed before) to look for data that might not be well-presented in previous learnings. Monitor statistics and monitor data as a living entity to determine when models need to be respun or data quality revisited.

 8. Don’t fall for Big Data hype.

Understand that applications should have prescriptive data requirements, search for the best quality data, and set up proper rights of use terms, governance and monitoring. Don’t take a “Big Data will tell us” view – most Big Data are liabilities vs. assets and it’s easier to focus on extreme data quality for key data feeds/elements rather than for the totality of data stores.

 9. Treat machine learning with caution.

Big Data Leaders look to be able to justify, explain and place value on each piece of data. Machine learning is touted to solve the malaise of Big Data. When machine learning finds patterns in data, data leaders ensure it’s not arbitrary correlations but look for causation.

10. Investigate data anomalies.

Does a new pattern represent a new behavior, manipulation of data feeds for crime, or new insights to understand?

In the era of advanced analytics and Big Data, data leadership is not just a techie thing, it needs to take place at the C level. As one of the first generation of Chief Analytics Officers, I take my responsibilities very seriously, and have frequent discussions of this changing role with other data leaders around the world.

So, what am I leaving out? Feel free to post your comments or drop me a note on Twitter @ScottZoldi.

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Modelling Deposit Price Elasticity: Challenges and Approach

This is the third in a series of blogs on deposit pricing, focusing on price elasticity modelling approaches and challenges.

The goal of any deposit price optimization solution is to make data-driven pricing decisions to manage portfolio balances and trade these off against the associated costs. These solutions should allow a pricing manager to prepare and run what-if analyses to assess the impact of pricing strategies, competitor price actions or movements in central bank base rates.

Fundamental to these solutions are price-elasticity models that capture and predict customer behavior as a response to pricing and other non-price factors. In this blog, we discuss the challenges and solution approaches for the development of robust price-elasticity models.

Price Response Signal

Price sensitivity can be measured with regards to product rate, market ranking, competitor rates or even interest paid to other products in the portfolio. The modelling challenge is not only to measure price sensitivity accurately, but to capture as much richness in pricing behavior as possible, e.g., variations in price sensitivity across different segments.

For example, ultra-low bank base rates have become the new normal in the US and Europe and it can be a challenge to isolate the price signal. There may be limited price variation in the modelling period or else one-time shocks caused by the presence of non-price related factors (such as Brexit) that drive balance flows. Previous experience of price sensitivity models across different markets, interest rate environments and transformation of price-related variables provides an anchor to avoid misdiagnosis of the price signal.


In order to truly understand the impact of pricing decisions, the flows between individual products and segments must be understood. This allows the prediction of the balance distribution by product and requires a tried and tested process for inferring balance flows. Understanding and predicting balance flows across products in turn allows pricing managers to assess the impact of cannibalization on pricing decisions and overall portfolio revenue.

Data Availability & Granularity

One of the biggest challenges for the development of price sensitivity models is data availability. The development of overly complex models with insufficient data results in the often-cited adage of garbage-in, garbage-out, so it is important to ensure that the modelling approach is appropriate to the data available.

Finer granularity, where more modelling segments are used, introduces richer pricing behavior and allows greater insights into pricing decisions. This must be balanced with the need for sufficient deposits within each segment to ensure a statistically significant price signal can be measured.

Another consideration concerns the model resolution: An established deposit portfolio with relatively stable balances might only re-price a few times a year and a monthly granularity is sufficient. On the other hand, a smaller bank that needs to make shorter-term funding decisions might buy balances through the introduction of market-leading rates. This type of pricing action typically occurs more frequently and would therefore need a weekly granularity.

Modelling Methodologies

Depending on a bank’s objectives and the availability of data, there are a number of modelling approaches that might be considered.

Deposit Price Elasticity Modelling Approaches FICO Modelling Deposit Price Elasticity: Challenges and Approach

Model Management

It is vitally important that all stakeholders from the pricing analyst to senior management have confidence in pricing models. The best way to achieve this is to ensure full transparency of the models, methodology and the factors that drive predictions.

With a full understanding of model drivers, the business can justify why particular pricing decisions are made to internal stakeholders and also answer any challenges posed by external regulators that a “black box” solution could not.

An ongoing model management process should monitor model performance against established accuracy thresholds to guide model recalibration and redevelopment. This ensures that models respond to changes in the market and provides a mechanism whereby the impact of recent pricing decisions feed back into the price sensitivity models.


In order to develop deposit price sensitivity models, careful consideration is needed to evaluate an organization’s requirements and mitigate some of the challenges discussed here. The deployment of such models offers substantial improvements on approaches that rely exclusively on expert judgment. It allows banks to more effectively manage their deposit portfolio, better understand their customer behavior and preferences, and identify new revenue opportunities. It is also a critical component on the journey towards full price optimization where banks derive all the benefits that data driven models have to offer.

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Double-Digit ATM Compromise Growth Continues in US

ATM Hacked Double Digit ATM Compromise Growth Continues in US

While data breaches and ransomware grab the headlines, we’re still seeing fraud growth due to ATM compromises in the US. The fraud growth rate has slowed down from the gangbusters surge we saw in 2015, but consumers and issuers still need to pay attention.

The latest data from the FICO® Card Alert Service, which monitors hundreds of thousands of ATMs and other readers in the US, shows a 39 percent increase in the number of cards compromised at US ATMs and merchants in the first six months of 2017, compared to the same period in 2016. The number of POS device and ATM compromises rose 21 percent in the same period.

Beyond the numbers, at FICO we have seen the rate of fraud pattern changes accelerating over the last two years. As criminals try to beat the system, we are continually adapting the predictive analytics we use to detect compromises.

This year marks the 25th anniversary of FICO’s work in AI and machine learning, which began with Falcon Fraud Manager. Ever since then, we have been in an innovation race with the bad guys. The latest figures show they’re not slowing down. Neither are we.

Tips for Consumers

  • If an ATM looks odd, or your card doesn’t enter the machine smoothly, consider going somewhere else for your cash.
  • Never approach an ATM if anyone is lingering nearby. Never engage in conversations with others around an ATM. Remain in your automobile until other ATM users have left the ATM.
  • If your plastic card is captured inside of an ATM, call your card issuer immediately to report it. Sometimes you may think that your card was captured by the ATM when in reality it was later retrieved by a criminal who staged its capture. Either way, you will need to arrange for a replacement card as soon as possible.
  • Ask your card issuer for a new card number if you suspect that your payment card may have been compromised at a merchant, restaurant or ATM. It’s important to change both your card number and your PIN whenever you experience a potential theft of your personal information.
  • Check your card transactions frequently, using online banking and your monthly statement.
  • Ask your card provider if they offer account alert technology that will deliver SMS text communications or emails to you in the event that fraudulent activity is suspected on your payment card.
  • Update your address and cell phone information for every card you have, so that you can be reached if there is ever a critical situation that requires your immediate attention.

If you’re interested in seeing card fraud trends in another part of the world, check out our European Fraud Map. And follow my fraud commentary on Twitter @FraudBird.

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Digital Transformation: Visualize Your Journey, Not Your Rival’s

Digital Transformation Digital Transformation: Visualize Your Journey, Not Your Rival’s

This is the second post in this series – read post one here.

Marcel Proust wasn’t ruminating about digital transformation when he said, “We don’t receive wisdom; we must discover it for ourselves after a journey that no one can take for us or spare us.” But this statement could well align with what businesses face in their DX evolution. Putting a new vision to work — and ultimately altering the fabric of your business — is a process that will be different for every organization.

That means you really can’t just copy what your competitors are doing and try to do it better — because by the time you’ve emulated them, they’ve moved onto the next digital iteration of their business. Instead, you need to model your journey based on your own DNA — factoring in people (the entire ecosystem, including your people, customers, partners, etc.), your products and services (existing and planned), current and future vision, and the list goes on.

With DX, however, your outcomes will never exactly mirror even your best-laid plans — instead, you’ll find yourself consistently unleashing new initiatives, shifting channels and partners, and realigning your business (perhaps more frequently than ever) to put the right people in the right places at the right time.

Discovering the Unknown

Take Jaguar Land Rover, the largest automotive manufacturer in the UK and a truly globally renowned brand name. They kicked off an initiative to exploit the unknown, by connecting all their databases to find problems that they didn’t knew existed.

Through this process, which included transformational capabilities such as mathematical optimization and machine learning, they found hundreds of data correlations previously hidden to their business. For example, one correlation helped identify a faulty accumulator in a set of 96 machines making cylinder blocks. As a group their performance was in the normal range, but closer analysis of energy consumption vs. productivity suggested there were outliers of “unusual transactions” – and the team discovered that an accumulator was working inefficiently, drawing excess energy.

In this regard, Jaguar Land Rover set off on an approach that was truly unique to their organization, because they didn’t know what they would find once they started looking. That’s what we mean when we say “visualize your digital business” – likely, the company’s competitors would, given a similar set of circumstances and strategies, uncover very different unknowns, and thus head down a different path of discovery and, ultimately, transformation.

So while it’s valuable to study what others in your industry are doing (and even cross-industry, with so much convergence happening today – witness Amazon acquiring Whole Foods), it’s valuable to remain mindful that the specific DNA within your business may not be applicable to emulating someone else’s success. Taking a trip down the path to the unknown, as Jaguar Land Rover is doing, will likely create your own successes that indeed, others will want to emulate.

For more on the Jaguar Land Rover “Exploiting the Unknown” discoveries, check out this article in The Manufacturer.

Next post, we’ll cover unleashing the knowledge hidden within your most critical assets – your people.

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Measuring the Cybersecurity Risk of Your Extended Enterprise

“What gets measured, gets managed,” is one of executives’ go-to quotes, and with good reason. But unfortunately, many of the things that keep executives awake at night, such as the cybersecurity risk of their extended enterprise, have been impossible to measure, let alone manage, at least until now.

FICO recently announced new capabilities for identifying and scoring 4th party risk with the FICO® Enterprise Security Score, allowing organizations to:

  • Pinpoint specific 4th party risks
  • Identify concentrations of risk throughout their extended enterprise and across common cloud services.

This is important because managing your own firm’s cybersecurity risk now involves understanding the risk of a much broader “extended enterprise.”

Connectivity Creates Aggregate Risk

The extended enterprise is not a new term, but in today’s hyper-connected environment, it takes on new meaning. Large companies may be connected over the internet to tens of thousands of business partners, each of which, in turn, may be connected to thousands more. These “4th parties”—the partners of partners—represent an additional, significant cybersecurity threat, as they can be conduits to all manner of threats that eventually strike within the metaphorical “four walls” of any given enterprise.

Aggregate risk is a familiar concept in the property and casualty (P&C) insurance industry, in which the extreme impact of a common set of threats — such as a major hurricane — can affect a large portion of their portfolio of their business. Aggregate risk modeling helps insurance companies take appropriate steps toward mitigation, including portfolio diversification and allocating appropriate capital to cover claims in case there is a major disruption.

Data breaches and malware attacks are the hurricanes and earthquakes of the rapidly growing cybersecurity insurance industry. Multiplied across the millions of interconnected businesses within the US alone, a single large cyber attack could trigger a similarly large number of claims, posing systemic risk to the insurance industry itself. But given the complex web of connectivity between most large enterprises and their extended network of business partners, it has been nearly impossible to identify and quantify aggregate risk.

A Score That Identifies 4th Party Risk

FICO has enhanced the FICO® Enterprise Security Score to identify the specific 4th party risks of scored organizations. While other approaches involve the application of industry averages, the FICO® Enterprise Security Score now helps breach insurers and enterprise vendor management teams to identify the specific vendor dependencies of their clients and business partners (including deployed IT components), and see the Enterprise Security Score of these 4th party relationships.

The service also helps users to identify common 4th party dependencies across a portfolio of 3rd party relationships. Breach insurers can now understand aggregate risk concentrations across a portfolio of policies, where multiple insureds may be exposed to common IT suppliers and technologies.

The FICO® Enterprise Security Score performs a complex assessment of an organization’s network assets, applies advanced predictive algorithms, and then condenses the results down to a three-digit score that rank-orders based on the odds of breach for the organization. The solution is now enhanced with key IT vendor and cloud service provider information for most organizations, allowing appropriately credentialed users to evaluate the risk of the extended enterprise.

Manage the Big Picture of Cyber Risk

Identifying 4th party risks is an increasingly important consideration for both enterprises and breach insurance carriers. These groups are concerned, respectively, about hidden, aggregate risk exposures across the extended enterprise and their portfolio of insureds. With the FICO® Enterprise Security Score, both can now better measure and manage 4th party risk.

Follow me on Twitter at @dougoclare.

Cybersecurity Risk 4th Party Measuring the Cybersecurity Risk of Your Extended Enterprise

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The Future of AI: Smart Machines Will Save Us, Not Destroy Us

AI 25 Years FICO Machine Learning The Future of AI: Smart Machines Will Save Us, Not Destroy Us

To commemorate the silver jubilee of FICO’s use of artificial intelligence and machine learning, we asked FICO employees a question: What does the future of AI look like? The post below is one of the thought-provoking responses, from Shafi Rahman, a principal scientist at FICO, working in San Diego.

The most memorable scene in the film 2001: A Space Odyssey is when HAL 3000 turns off the life-support functions of the crew in an attempt to keep itself from getting disconnected. This was my first encounter with what we call artificial intelligence. Then in Terminator, we were introduced to Skynet, a neural network based AI, which raised an army of robots for self-preservation.

Hollywood has always portrayed the future of AI in a dystopian, cataclysmic manner. In reality, I believe things will be different — benign and beneficial to the human-kind.

In the last 10 years, we have seen huge revival in the research and development of AI. This has been fueled primarily by our ability to train deep neural networks using larger and larger training datasets, riding on faster and better CPUs and GPUs, Big Data and innovations in algorithms.

AI is poised to evolve faster than ever in the next decade. And yet, we are not going to witness the kind of man-versus-machine showdown that we have seen in Hollywood movies. On the contrary, AI will lead to major improvements in all dimensions of our lives within the next 10 years.

AI Will Save Lives

Take the ongoing research in the application of AI in medical sciences. The early results coming out of labs are nothing but positive. Samsung, for example, announced last year that its ultrasound devices can now identify breast cancer and even advise whether it’s benign or malignant, using AI that analyses ultrasound images. Similarly, using AI based on speech recognition and analysis, scientists have devised early detection capability for onset of Parkinson’s disease, with the claimed detection rate of 99%. Merck gave huge cash prizes for discovering new drugs using AI.

In fact, there is an entire research journal, Artificial Intelligence in Medicine, dedicated to the research in the application of AI in medicine, human biology and health care. This rapid progress leads me to believe that within the next 10 years, there will be faster and earlier diagnosis of all diseases at a much lower cost, and the drugs and treatments will be much more effective. Doctors could routinely use AI systems for second opinions, and medical mistakes would be things of the past. These would lead to saving of millions of lives across the globe each year, and improve quality of medical outcomes dramatically.

AI Will Make Workers’ Lives Better and the Earth Greener

In the past, automation has saved people from drudgery and elevated the quality of life by making work environment safer, while letting us do more intellectually satisfying and physically less demanding jobs. In the current wave of automation, led by the resurgence in AI, I don’t expect the outcome to be any differently, though like earlier, there will be disruptions.

The American Trucking Associations predicts that a total of nearly 900,000 new drivers will be needed over the next eight years and there are 50,000 jobs currently vacant due to lack of drivers. This is the kind of scenario where AI would step in and fill the gap. As Harvard Technology Review reports, any self-driving truck will require a driver to remain on board to take over the control in cases which AI can’t handle. The job itself will become less stressful, leading to fewer mistakes and road accidents. The trucks will run continuously on the road, reducing the time and cost and making things cheaper. The fuel efficiency will go up by as much as 30% when AI takes control, leading to a greener planet.

If the use of AI in trucking industry is any indication, in the next ten years, jobs will often require interplay with AI based systems. It will reduce effort and increase efficiency of the workers. The side effects would almost always include better quality of life, better and cheaper products, happier employees and customers and a green earth. Societies would evolve to help those affected by the job displacements, while continuous and on-the-job learning would become de facto standard.

AI Will Reduce Cyber-Crime and Money Laundering

At FICO we have been leveraging artificial intelligence to help our clients make their decisions smarter. It has been 25 years since we got our first patent in application of AI for fraud detection in the payment card space. This led to a rapid decline in card fraud across all of the USA within a very short time.

Since then we have extended the AI offerings to include self-learning models, and its application in diverse spaces like cybersecurity. We expect that in the next 10 years, the AI capabilities of FICO can dramatically reduce the threat of cyberattacks on computer networks and connected IoT devices by identifying even the most subtle forms of intrusions and isolating vulnerabilities. In the next 10 years, our AI capabilities could make money laundering and terrorism financing virtually impossible. One can even imagine the same technology being extended to make it impossible for criminals and terrorists to hide their activity and avoid scrutiny. In conjunction with AI-based facial recognition system, it would be impossible for them to avoid detection.

AI for Good, Not Evil

Predicting the future is a hard business even for those of us who make living out of using AI to predict future outcomes. Progress of technology is not always linear. It does lead to disruption. Unintended things happen. But then we learn to deal with the negative fall-outs, do a course correction and steer the technology towards the greater good.

The path of the future of AI shouldn’t be any different. And there is already a good line of sight into what the future of AI would be like in the next 10 years. So while things may get difficult at times, I expect that we won’t be encountering Skynet or HAL anytime soon. On the contrary, we will have healthier population, happier employees and customers, more profitable businesses, less crime and a safer planet.

See other FICO posts on artificial intelligence.

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Three Keys to Advancing your Digital Transformation

Digital Transformation Three Keys to Advancing your Digital Transformation

With today’s proliferation of data, digital transformation (DX) has become more than a hot topic: It’s an imperative for businesses of all shapes and sizes. The collision of data, analytics and technology has businesses, analysts and consumers excited — and scared — about what could happen next.

On one hand, everyone from banks to bagel shops and travel sites to tractor manufacturers have found new ways to connect the dots in their businesses while forging stronger, more dynamic customer engagement. Artificial intelligence (AI) has come of age in technologies such as smart sensors, robotic arms, and devices that can turn lights and heat on and off, adjust for changes in conditions and preferences, and even automatically reorder food and supplies for us.

However, today’s Chief Analytics Officer (and Chief Data Officer and Chief Digital Officer, for example) faces both the promise and precariousness of digitizing business. While significant opportunities abound to drive revenues and customer connectivity, any leader will freely confess there are myriad technological, business and human obstacles to transforming even one element of business, introducing a new unique product or even meeting regulatory requirements.

The Big Data Dilemma

Big Data is at once the promise of the DX and its biggest roadblock. A recent Harvard Business Review article put it succinctly: “Businesses today are constantly generating enormous amounts of data, but that doesn’t always translate to actionable information.”

When 150 data scientists were asked if they had built a machine learning model, roughly one-third raised their hands. How many had deployed and/or used this model to generate value, and evaluated it? Not a single one.

This doesn’t invalidate the role of Big Data in achieving DX. To the contrary: The key to leveraging Big Data is understanding what its role is in solving your business problems, and then building strategies to make that happen — understanding, of course, that there will be missteps and possibly complete meltdowns along the way.

In fact, Big Data is just one component of DX that you need to think about. Your technology infrastructure and investments (including packaged applications, databases, and analytic and BI tools) need to similarly be rationalized and ultimately monetized, to deliver the true value they can bring to DX.

Odds are many components will either be retired or repurposed, and you’ll likely come to the same conclusion as everyone else that your business users are going to be key players in how DX technology solutions get built and used. That means your technology and analytic tools need to allow you the agility and flexibility to prototype and deploy quickly; evolve at the speed of business; and empower people across functions and lines of business to collaborate more than they’ve ever done before.

Beyond mapping out your overarching data, technology and analytic strategies, there are several areas to consider on your DX journey. Over the next three posts, I’ll focus on how to:

  1. Visualize your digital business, not your competitors’
  2. Unleash the knowledge hidden within your most critical assets
  3. Embrace the role and evolution of analytics within your journey

To whet your appetite, check out this short video on the role of AI in making DX-powered decisions.

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