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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.”  

The post SPDB Credit Card Centre Automates Early Collections appeared first on FICO.

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It’s a great question, and needs to be asked. Cyber scores and ratings have been around for some time now, gaining steady momentum over the last five years. That said, the market for security risk assessment scores and ratings remains nascent, with a double-digit CAGR that will likely continue into the foreseeable future. With new data protection and privacy regulations coming online — such as the EU’s General Data Protection Regulation (GDPR) and the California Consumer Protection Act (CCPA) –– interest in understanding and managing cyber risk is at an all-time high. A drumbeat of high-profile breaches underscores the risk, and the new regulations demand more diligence in managing first-party (your company) and third-party (supply chain) risk. Third-Party Risk Is Top of Mind The latter topic (third-party risk) is increasingly important. While organizations can readily gain some insight into their own security posture (and a second opinion from one of the commercial cyber rating firms, if they want it), the appeal of an independent, non-intrusive assessment of supply chain partners’ cyber risk is clear and compelling. The same is true for cyber insurance brokers, underwriters and reinsurers. As more carriers enter the market (intensifying competition) and coverage moves down-market (requiring carriers to underwrite policies with lower premiums and less information), there is an acute need for an efficient, accurate way to assess cyber risk. What Do Cyber Scores Mean? As organizations that have done proof-of-concept pilots with cyber scores or ratings consider exactly how to leverage them in supply chain decision workflows, the question naturally comes up: Just what does this score actually mean? Insurance carriers using these scores and ratings to underwrite and price cyber risk policies are asking the same thing. At FICO, we encourage you to ask. If you’re using one of these scores/ratings, or are considering doing so, you deserve an up-front answer. The reality is that some of the providers in this space can’t answer the question. The scores or ratings they produce are generated by judgmental scorecards that apply “informed but arbitrary” weighting to myriad risk signals they collect. Certainly there are experts in these companies who can render a directionally correct opinion on any given input –– but the weights assigned to these signals have no statistical basis or mathematical foundation. Their relationship to actual security outcomes was never established. And for that matter, what specific security outcome are they attempting to measure? When you compile a score based on multiple signals that are evaluated in this way, without a well-defined objective outcome, you really don’t know what you are measuring. A Score Built on Real Data and Sound Methods At FICO, we take a different approach. And we have the experience, tools, methods and data to back it up. FICO’s Cyber Risk Score is empirically derived, with a transparent and documented objective outcome. Our model is built to forecast the likelihood of a material breach event in the next 12 months. It’s not an opinion, a current-state assessment, or an arbitrary grade attached to a long list of potential security vulnerabilities. The FICO Cyber Risk Score translates directly to the “event odds” of a material breach occurring in a specified time period (12 months from the score date). It is built using the measured correlations between signals and the objective outcome. Subscribers are provided with a detailed model report that describes the objective outcome, outlines the score-to-odds relationship, and exposes the population distribution across the score range. FICO’s users know exactly what the score means. The veracity of our approach and the transparency behind the meaning of FICO’s Cyber Risk Score are key reasons why Chartis Research recently named FICO a category leader in Cyber Risk Quantification solutions. You can read their analysis of FICO here. We’re proud of the recognition, but even more proud that we’re able to answer the question, “What does the FICO Cyber Risk Score mean?” If you’re using a competing score, we encourage you to ask that question of your provider. If you don’t like the answer, give us a call or visit https://cyberscore.fico.com. Follow me on Twitter @dougoclare.  

The post Cyber Scores: What Do They Mean? appeared first on FICO.

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When designing a strategy for detecting and preventing fraud, everyone always comes to the same conclusion—there is no silver bullet. There are simply too many variables, and too much change in technology, customer behavior and fraudsters’ tactics for any one solution to work effectively and sustainably for every organization, no matter how sophisticated. Consequently, experienced fraud management executives are constantly experimenting and evaluating new data sources, scores, models, algorithms and technologies for that competitive edge. They observe customers’ behavior, survey their preferences and maintain a working knowledge of fraudsters’ evolving tactics. The goal is the same for everyone—minimize fraud losses while effectively balancing customers’ experiences and operational expenses. But the exact recipe each organization lands on—the mix of processes, people and products—varies widely and changes constantly. Fraud Models - Five keys to finding the right fraud score Many different providers—whether associations, processors, switches or analytic firms—have begun to offer unique fraud scoring models, targeting different products, channels and customer segments. There are also many fraud platforms that allow organizations to build and deploy their own internal models. So, which fraud scores will be most effective for your organization? There really isn’t a secret formula, but there are some basic principles, gleaned from years of experience working with industry leaders, to incorporate into your organization. First, you should know that leveraging multiple fraud scores is a perfectly fine practice. Each vendor has different techniques and algorithms to produce their fraud score. Every technique has its own advantages and disadvantages. While it is important to understand the underlying technology driving the different fraud scores, what is more important is the performance and effectiveness of each fraud score, and whether it solves your business challenges. Second, you should not underestimate the power of consortium data. The sources, quality and quantity of data is a critical component in developing robust models. Be mindful of startup vendors with a minimal client base touting consortium models. A good consortium should be representative of the industry it is representing. Third, some fraud scores are now “mandatory.”  What this means is that a provider (scheme/association or processor/switch) may be requiring the use of their fraud score, but don’t be afraid to question and quantify the effectiveness of the fraud score. Fourth, measure the effectiveness of fraud scores. You can measure model performance effectiveness in a dozen different ways. What is important is that you are using the same approach and methodology across all fraud scores. Never apply performance metrics you have received from one vendor across all other vendors, as they are all likely using different ways to measure performance. A simple metric like value detection rate can be measured in several different ways. Find a common suite of performance metrics you can measure against all fraud scoring models. Lastly, don’t forget about cost and benefit. Understanding the cost should always be part of your evaluation of fraud scores. The benefit is equally important, as it is in any performance comparison. Do your fraud scores overlap in some areas? Can one model be utilized for part of your portfolio and another model for the other portfolios? As fraud continues to evolve, so should fraud technology and scoring models. There are now a number of different providers out in the market that provide effective scoring models. As such, you shouldn’t rely solely on one provider; rather, leverage as many as possible. And lastly, ensure a fair model comparison has been completed utilizing a common suite of key performance. +++ Drew Manuel is a senior director within the Fraud, Security and Compliance unit of FICO Advisors. He has over 24 years' experience in the fraud industry and is regularly called upon to do fraud model/score reviews by clients around the world. Enjoyed this blog? Why not read this one too. Does Your Fraud Department Have the Right KPIs?

The post Got Enough Fraud… Models That Is? appeared first on FICO.

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When designing a strategy for detecting and preventing fraud, everyone always comes to the same conclusion—there is no silver bullet. There are simply too many variables, and too much change in technology, customer behavior and fraudsters’ tactics for any one solution to work effectively and sustainably for every organization, no matter how sophisticated. Consequently, experienced fraud management executives are constantly experimenting and evaluating new data sources, scores, models, algorithms and technologies for that competitive edge. They observe customers’ behavior, survey their preferences and maintain a working knowledge of fraudsters’ evolving tactics. The goal is the same for everyone—minimize fraud losses while effectively balancing customers’ experiences and operational expenses. But the exact recipe each organization lands on—the mix of processes, people and products—varies widely and changes constantly. Fraud Models - Five keys to finding the right fraud score Many different providers—whether associations, processors, switches or analytic firms—have begun to offer unique fraud scoring models, targeting different products, channels and customer segments. There are also many fraud platforms that allow organizations to build and deploy their own internal models. So, which fraud scores will be most effective for your organization? There really isn’t a secret formula, but there are some basic principles, gleaned from years of experience working with industry leaders, to incorporate into your organization.

First, you should know that leveraging multiple fraud scores is a perfectly fine practice. Each vendor has different techniques and algorithms to produce their fraud score. Every technique has its own advantages and disadvantages. While it is important to understand the underlying technology driving the different fraud scores, what is more important is the performance and effectiveness of each fraud score, and whether it solves your business challenges.

Second, you should not underestimate the power of consortium data. The sources, quality and quantity of data is a critical component in developing robust models. Be mindful of startup vendors with a minimal client base touting consortium models. A good consortium should be representative of the industry it is representing.

Third, some fraud scores are now “mandatory.”  What this means is that a provider (scheme/association or processor/switch) may be requiring the use of their fraud score, but don’t be afraid to question and quantify the effectiveness of the fraud score.

Fourth, measure the effectiveness of fraud scores. You can measure model performance effectiveness in a dozen different ways. What is important is that you are using the same approach and methodology across all fraud scores. Never apply performance metrics you have received from one vendor across all other vendors, as they are all likely using different ways to measure performance. A simple metric like value detection rate can be measured in several different ways. Find a common suite of performance metrics you can measure against all fraud scoring models.

Lastly, don’t forget about cost and benefit. Understanding the cost should always be part of your evaluation of fraud scores. The benefit is equally important, as it is in any performance comparison. Do your fraud scores overlap in some areas? Can one model be utilized for part of your portfolio and another model for the other portfolios?

As fraud continues to evolve, so should fraud technology and scoring models. There are now a number of different providers out in the market that provide effective scoring models. As such, you shouldn’t rely solely on one provider; rather, leverage as many as possible. And lastly, ensure a fair model comparison has been completed utilizing a common suite of key performance. +++ Drew Manuel is a senior director within the Fraud, Security and Compliance unit of FICO Advisors. He has over 24 years' experience in the fraud industry and is regularly called upon to do fraud model/score reviews by clients around the world. Enjoyed this blog? Why not read this one too. Does Your Fraud Department Have the Right KPIs?

The post Got Enough Fraud… Models That Is? appeared first on FICO.

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  Want to protect yourself online - and protect your family - from data compromise and identity theft? FICO's Doug Clare, vice president for cybersecurity solutions, offers some advice in this interview with NBC King 5 News in Seattle. He was interviewed in conjunction with his talk at the US Chamber of Commerce Cybersecurity Series, where he spoke about cyber risk and third-party risk management. [video width="1280" height="720" mp4="https://www.fico.com/blogs/wp-content/uploads/2019/06/Doug-Clare-Cyber-Video.mp4"][/video] "It’s important for consumers to realize that when they do business with somebody, they may be doing business with more than one party," Clare said. "Everybody’s got a supply chain." For consumers, protection means paying close attention. "It’s important to stay vigilant, particularly about email," Clare said. "If you get an email with a link, check it out, and don’t click it until you’re sure. Make sure the email is coming from who you think it’s coming from, that the domain name on the email address is correct. Email is a big challenge, be careful." Clare also urged viewers, "Have that conversation with your kids and your parents." Third-party risk management is a hot issue in the world of cybersecurity, since vulnerabilities in a firm's supply chain, partner or vendor networks can expose sensitive data. It's estimated that half of all data breaches occur through third parties. According to Ponemon Institute’s 2018 Data Risk in the Third-Party Ecosystem, more than 60 percent of US CISOs have indicated being the victim of a third-party breach incident. More tips on how to protect yourself online are discussed in this blog post and video.

The post Cybersecurity: How to Protect Yourself Online (Video) appeared first on FICO.

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Fraud is a serious concern for the communications industry, with proceeds and services used by organised crime and terrorist networks. Whilst reliable statistics are difficult to come by, industry association Communications Fraud Control Association estimates the total global fraud loss at around $29 billion annually, according to a CFCA 2017 Global Fraud Loss Survey. Of this, over $ billion n is attributed to subscription fraud and account takeover. In an industry which is undergoing widespread digital transformation to streamline customer journeys and reduce operating costs through automation, there are clear implications and requirements to prevent fraud. Defining Subscription Fraud Subscription fraud occurs when a fraudster uses their own, a stolen or a synthetic identity to obtain mobile devices and services with no intention to pay. As the wholesale, and retail, purchase cost of devices has increased over recent years, a grey market has been created and exploited by fraudsters obtaining devices to resell for lucrative profits. The mobile device subsidy model that is still prevalent across most of Europe, whereby customers have little to no outlay upfront and receive an expensive device that can be resold, lends itself to a high-margin business model for the unscrupulous. There are numerous other drivers for subscription fraud, including;
  • A proliferation of digital identity information. The ‘want it now’ culture of today’s consumer means that the technologies we all love to use for their convenience make it easier for fraudsters to do what they do, e.g., bots designed to test login credentials or to generate customer orders using stolen or synthetic identities.
  • An increasing amount of data breaches across all industries. This results in organised fraudsters sitting on customer information until they decide to use it.
  • A ‘convergence’ of fraud. As many services now employ the smart phone as a contact point for verification, such as a One Time Passcode via SMS. The consumer’s mobile account has become fundamental as part of an authentication trail in many services such as banking. Fraudsters therefore target customers’ accounts in order not to defraud just the telecoms company but the customer themselves in order to manipulate their financial, or other services.
First-Party Subscription Fraud Solutions that offer identity verification and validation services are unable to address the critical question of first-party fraud — those customers that own the identity but have no intention to pay for the services or devices. And the question of intent can vex credit assessment and identity fraud systems. First-party subscription fraud looks like a credit risk issue of delinquency / default. A large proportion of fraud may be classified as bad debt which also has consequences on resource-stretched collections operations. If the company is trying to collect a debt where there is no intention to pay then it’s a waste of resource as well as a potential opportunity cost. The first-party fraud also includes a significant proportion of ‘mules’, with university students often targeted. They are instructed to apply for as many devices as they can in return for the promise of quick cash. Often, the mule doesn’t consider the impact this will have on their future credit rating, or maybe they don’t care, as is the case with some foreign students taking out devices shortly before returning overseas and leaving the debt behind them. [caption id="attachment_38461" width="576"] Source: FICO Blog[/caption]   And of course, once a subscription is activated and in the hands of an organised fraudster, the subscription fraud can then be the forerunner to commit other types of telecoms fraud, such as interconnect bypass and international revenue share fraud. Recognising Intent to Pay It’s obviously better to prevent the fraudulent contract being taken out in the first place rather than pick it up through network traffic monitoring. By that point a chunk of the damage is already done and, if there was a device provided, it is now a financial loss. Whilst it’s easy to point out the obvious and that CSPs could be preventing subscription fraud at the point of sale, the reality is much more difficult. As mentioned above, being able to identify the intent to pay isn’t easy. Couple this with onboarding and upgrade processes that are designed to minimise friction in the customer process, and fraud teams need to be sophisticated in how they identify subscription fraud without impacting the customer journey for the genuine customers. There are a number of different types of fraud modus operandi to contend with too, from organised gangs, to opportunists tempted to take advantage of poor process or loopholes, to ‘sleeper’ fraudsters who bide their time before conducting fraudulent activity, to ‘hit and run’ fraud that is aggressive in terms of speed and value stolen. In my next post, I’ll discuss account takeover fraud, and then how to tackle these kinds of fraud.

The post What Is Telecom Subscription Fraud? appeared first on FICO.

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A recent survey by analytics software firm FICO has revealed that three in five (60%) banks in Asia Pacific have yet to offer a full digital account opening process for new customers, despite recent reports that nearly 9 in 10 financial institutions in the region embarked on digital transformation. Full Digital Account Opening - Challenges The region’s changing regulations (28%) and the need to create digital know-your-customer (KYC) and anti-money laundering (AML) (21%) solutions were cited as the two key challenges for APAC banks looking to acquire new customers online. “In Asia, the identification processes used for services such as e-government, banking or telecommunications evolved independently of each other, leading to a fragmented approach with inconsistent levels of security,” said Dan McConaghy, president of FICO in Asia Pacific. “Open banking and regulations like Europe’s PSD2 are now bringing regulatory rigor to bear on the issue and forcing banks to comply to certain standards and embrace technologies that will better enable digital onboarding.” Full Digital Account Opening - Separate Digital Offerings For some established banks, one short-cut to their own existing challenges, incumbent technologies and inefficient silos is to start again. FICO’s survey revealed that 79 percent of the banks have launched or are currently considering a separate digital banking offering to leapfrog challenges in acquiring and retaining new customers. “Fintechs and challenger banks have disrupted the status quo in the financial services universe,” said McConaghy. “By developing compelling new products, services and experiences, these companies have set a new standard and raised customer expectations. Traditional banks now find themselves needing to rethink and redesign their services, to transform themselves digitally, and meet the market.” FICO’s survey found that 40 percent of respondents said digital-only banks and fintechs were the greatest competition to their business, with APAC Internet players (20%) and telcos diversifying into lending (20%) coming in equal second place. Conversely, the greatest opportunities for digital banking for the respondents were nominated as digital payments (32%) and personal loans (24%). APAC banks said they are planning to focus on investing in data science (19%) and improving their customer segmentation for products and services (19%) as their top priorities for their bank transformation. Full Digital Account Opening - Impact On Bank Size When asked about the future size of their bank in the year 2030, some 28 percent of the survey respondents predicted that their organisations would need fewer employees (between 5 to 50% decrease). A further 28 percent said they think they will need significantly less staff (a 50% or more decrease) in 2030. “There is some recognition in the market that the right size for many banks in the near future will be smaller,” said McConaghy. “New technologies, increasing competition and different business models will change the way financial services looks in ten years’ time.” FICO surveyed 20 chief risk officers from across Asia Pacific in April 2019 at its CRO Forum 2019 event in Bangkok, Thailand. https://www.youtube.com/watch?v=cXSi3_agADU&t=10s

The post 3 in 5 APAC Banks Do Not Have Full Digital Account Opening appeared first on FICO.

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Telecoms today face many challenges, including high roll rates and expensive high-touch contact strategies. A key challenge is to more effectively communicate with customers. A growing number of consumers prefer to conduct business on their mobile devices, and many have a preference for the type of communication they receive, whether it’s via IVR, email, or SMS. Telecoms are struggling to establish best practices around how to optimize these interactions. Telecoms have to do more with less.  By deploying advanced analytics, including AI and Machine Learning, they can gain a greater understanding of customer expectations and experiences. By automating processes and improving the customer experience, we have actually seen delinquency rates drop by 40%, collection costs drop by 15%, and a reduction in the number of days it takes to collect. Customer experience and satisfaction has been ranked a number-one business priority by a large majority of the top telecoms, globally. By incorporating automation into their business, they can deliver the value of the company’s best-performing agents, while flexibly scaling them to handle whatever level of capacity is required on any particular day. These new tools help telecoms deliver the right message for a consumer, and convey it via their preferred channel at the right time – thus maximizing the likelihood of a successful outcome. FICO and Cox Communications recently presented a webinar on “Telecom Trends and Best Practices in Omni-Channel Collections.”A recording is available at: https://content.fico.com/l/517101/2019-04-04/7xfg5

The post Telecoms Leverage Omni-channel Collections to Reduce Churn and Improve the Customer Experience appeared first on FICO.

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What innovations in AI and advanced analytics can propel your digital transformation? This is the theme of FICO’s major global conference, FICO World 2019, November 4-7 in New York. With more than 90 sessions, FICO World attendees will hear the latest case studies, analytic innovations and best practices for making real-time customer decisions with AI and optimizing business results. Industry veterans and innovators will share insights, know-how and advice for using analytics in financial services, banking, automotive finance, mortgage lending, telecommunications, insurance and regulatory compliance. Held at the New York Hilton Midtown, FICO World is the leading international conference on big data analytics and decision technology, bringing together thought leaders and innovators from around the globe. Want a preview of the excitement? Watch this short video, then learn more and register at www.fico.com/ficoworld.
FICO World 2019 - Better Decisions with AI - YouTube

The post Video: Explore Better Decisions with AI at FICO World 2019 appeared first on FICO.

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In this series, I discussed how digital transformation is reshaping the origination processes and improving the customer experience. The previous two blogs have looked at how automated systems can be used to drive new business growth, and how it can sustain current customers by improving the customer experience. For the final installment in the series, I want to discuss how analytics can transform the offer determination and decision accuracy process. Utilizing Analytics to Streamline Offer Determination Prescriptive analytics can be used to evaluate all possible offer combinations and identify which ones will maximize target performance metrics while adhering to organizational constraints. Doing so can lead to more flexible offers for consumers and increased sales for dealers, without compromising risk or compliance standards. For example, we all know that a trip to the car dealership can be tedious and anxiety-inducing. However, auto finance providers are looking to make it easier for customers to purchase a new vehicle by leveraging predictive analytics to better understand how specific offer terms will impact uptake, risk and profitability. Finance providers that do not feel ready to fully automate this alternative deal structure can simply send the offers generated to an underwriter, who can manually select the deals they feel comfortable with.  This same capability can be used across the credit lifecycle, notably by helping modify terms with customers who go into arrears. The customer keeps their vehicle, while the lender reduces the number of write-offs and significantly increases the customer’s loyalty to the brand. Decision Accuracy in Risk Determination It’s difficult to accurately evaluate credit applications for risk in changing market conditions, and pricing that risk effectively. Risk managers worry about what happens if the economy changes course and cannot react quickly enough – or worse, they know the changes they need to make, but their IT team cannot make any changes to the production system for several months. As a result, leading banks are utilizing a combination of self-directed analytic techniques that employ the latest machine learning algorithms and structured scorecards to continuously identify better risk segmentation strategies and implement those strategies in a controlled and transparent manner. Banks that deal with high-risk users can leverage prescriptive analytics to evaluate all possible price points and identify which terms will maximize target performance metrics while adhering to organizational constraints. Origination decisions weigh heavily on future profitability; building analytics into the decisioning process helps to eliminate repetitious, time-intensive and often error-prone manual operations. Automating decisioning can also help banks significantly reduce measurable risk throughout the life of an account and settle the stage for long-term customer relationships, while providing the agility to quickly modify strategies to meet today’s shifting economic conditions. As the pace of business increases and customer expectations continue to shift, digital transformation will play an increasingly vital role within financial institutions. Whether it’s streamlining the originations process through automation, or improving customer communication with real-time and personalized communications, tomorrow’s success will lie in exceeding customers’ expectations. Previous blogs: Reinventing Originations in the Digital Era Reinventing Origination: Engage the Customer in the Process

The post Reinventing Origination: Fixing the Decisions that Matter appeared first on FICO.

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