Can Fraud Alerts Raise Customer Loyalty?

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1 Can Fraud Alerts Raise Customer Loyalty? Stop fraud faster while building trust by better managing the customer experience Number 64 December 2012 For most people, being contacted by a bank about potential fraud isn t a wonderful experience but it can be if it s managed in a timely, personalized manner. In fact, customers of a UK bank consistently point to fraud intervention as a golden moment of customer service in the organization s quarterly surveys. Mobile devices and electronic channels are enabling banks to engage customers, using the media of their choice, in intelligent automated dialogs that stop fraud in its tracks. With no increase in fraud management personnel expense, banks are stopping fraud faster, while building customer trust and loyalty in ways that drive account usage and spending. And by constantly feeding back to analytics the results of these customer interactions, they re improving the accuracy and adaptability of fraud detection systems. This white paper discusses how leading banks are driving gains by managing individual customer experiences as part of fraud intervention. We ll share some of the results they re seeing in terms of better fraud detection, increased efficiency and higher customer satisfaction. The paper also reveals how industry leaders are: Learn how one bank resolved 250% more fraud cases with 30% fewer declines, 95% customer satisfaction and zero FTE increase Stopping fraud earlier before losses mount. Finding more fraud without annoying customers. Overcoming channel/product silos for customer-level intervention. Making detection and action smarter by closing the loop between them. Adapting faster to changing fraud patterns. Make every decision count TM

2 Turning Moments of Truth into Gold It s been called a moment of truth. When a bank contacts a customer to check on a possible fraudulent transaction, the experience can be a negative or positive one for the relationship. In fact, customers regularly characterize such moments as golden in one UK bank s quarterly customer service surveys. This demonstrates how much opportunity there is to turn what could be an annoyance into a valued service. Timeliness and personalization are critical to better managing the customer experience. When a suspicious transaction is detected, banks must be able to reach customers where they are, through the best means for that individual given the circumstances. That means being able to make contact not only through calls to landline phones, but through calls, s and text messages to mobile phones, tablets, laptops and other devices. 43% of consumers surveyed want their bank to engage with them more on account updates, potential fraud and other non-marketing matters. FICO research, 2012 It also means engaging customers in an efficient automated dialog. For card transactions, instead of customers receiving a one-way communication, hours or days later, requesting they call the fraud line, most cases can be resolved within minutes, some in seconds. Instead of a legitimate transaction being declined, customers immediately confirm they are the one using the card, and can continue spending. And for online banking, instead of banks having to choose between letting a risky funds transfer go through and meeting regulatory requirements for processing times, they can quickly check with the customer before releasing the funds. When banks leverage such auto-resolution capabilities, customers can resolve suspected fraud cases on their account themselves, often simply by pressing a key on their phone or computer (see Figure 1). While there s always the option of talking with a fraud analyst, they don t have to because they can interact with a virtual analyst able to perform the same functions, including accessing records and updating the account in a direct, immediate and discrete manner. Customer feedback has been extremely positive for banks that have implemented this approach. At the UK bank mentioned above, 76% were highly satisfied with its auto-resolution service, 84% found the service easy to use and 89% said they had increased confidence in using their card again. Other banks report similar or even higher customer approval rates, as shown in Figure 2. Figure 1: Speedy fraud case resolution by SMS Figure 2: Strong customer approval of auto-resolution survey results from three FICO clients in the UK Bank 1 91% happy with auto-resolution 94% would recommend it to family & friends Bank 2 87% willing to receive contacts in future 78% agree strongly the technology has improved the overall level of service they re experiencing Bank 3 84% find auto-resolution easy to use 89% said auto-resolution has increased their confidence in using card page 2

3 »»»Managing» Individual Fraud Experiences This heightened customer satisfaction and loyalty aren t earned by punching out messages to customers with cookie-cutter contact strategies. They come from assessing multiple factors about the situation and the customer to form and execute an individualized strategy for contacting that customer in the right way with the right timing. As depicted in Figure 3, banks today need to couple intelligent, automated fraud detection with intelligent, automated decision making for customer experience management. And they need to be able to perform these functions, when appropriate, at high volumes in real-time. Figure 3: A closed loop between detection and action makes both smarter Customer transactions Detection LOW HIGH Score How risky is this transaction? (Single score across channels/products or multiple scores from different detection systems) Assess Decide Intervene Resolve What else do we know about this customer and this situation? Which communication channels are available and secure? Identify the best treatment for this customer, in this situation, at this point in time. Action! Contact strategy executed across one or more channels. Results monitored and strategy adjusted as needed. Legitimate or fraudulent transaction confirmed. Customer assisted. Results fed back to analytics to improve detection. Real-time feedback Figure 4: Auto-resolution speed metrics typical results from FICO clients worldwide Improvement in average time-to-case Most customer contacts made Reduction in average contact time Cases resolved within 20 mins 15 secs (from 20 + mins) within 30 secs 50% 67% Closing the loop between detection and action makes both smarter. Action is smarter: When assessing whether action is required, an autoresolution solution can consider an overall fraud score from a multichannel/ multiproduct detection system, or look at multiple scores and data from different channel- or product-specific systems. It may perform additional checks, such as looking to see if the telephone numbers and addresses on the account have ever been associated with suspicious activity in the past or match those on a fraud watch list. Banks can also, of course, create any number and variety of case generation/management rules to guide auto-resolution. To decide what action to take, the solution considers the customer s contact preferences and contact history (e.g., has she responded better to SMS or a phone call?). It looks at the time zone of where it appears the customer is currently located. It selects the right channel or combination/sequence of channels, avoiding any that may have been compromised. A good system, for instance, will check to see if there s been a change in a mobile phone SIM card since the last transaction or if an inbound caller ID has been altered. Detection is smarter: The resulting interventions generally resolve fraud cases swiftly, with results fed back to detection in a closed loop. That s a powerful page 3

4 »» advantage if the detection system includes an adaptive model able to use this feedback to adjust in real time to changing fraud patterns and customer behavior. This closed-loop approach also enables banks to deliver each customer a planned, managed experience that s dynamic and fully responsive to what is happening with the customer and in the production environment. As the possible fraud scenarios depicted in Figure 5 show, the delivery of this experience is a communication-enabled business process that extends across channels and products. Customers get a consistent experience because the virtual analyst works round-the-clock and covers all touchpoints. If a customer receives a contact through one channel and chooses to respond through another, the virtual analyst still knows who they are and everything that has transpired on the case. Figure 5: Auto-resolution examples, possible fraud scenarios based on best practices Detection Action Score Assess Decide Intervene Resolve LOW HIGH! Credit/debit card transaction High-risk transaction for large amount with merchant outside of country, not purchased from before. No notation about travel on file. Transaction declined. Other recent transactions in this country on other accounts? Changes to SIM card since last transaction? What time is it at transaction location and customer home location? If other transactions and no SIM change, unblock immediately. If no transactions, contact customer via SMS (customerpreferred method). If SIM change, send and leave voic on landline. Adjust strategy based on time zones. SMS inquiry to mobile phone sent within minutes of transaction denial. Customer confirms legitimate. Retried transaction approved. Uses card confidently during rest of trip. Online banking Wire transfer attempt looks like coming from customer (correct IP address, HTTPS certificate, one-time passcode, etc.), but behavior pattern is unusual. Malware may have intercepted and receiver account values changed. Faster Payments regulations apply, but transaction is high risk. Are payer/receiver account owners the same, or are there known relationships between them? Have there been any SIM card changes since last transaction? Put transfer on hold while using all available and secure channels to contact customer. Customer contacted simultaneously via smartphone app and voice calls to mobile and landline. Customer confirms fraud via smartphone app, so payment is released for settlement. Customer is provided with reassurance and next steps for re-establishing account security. Nonmonetary transaction New authorized user has just been added to debit account. Could be first sign of account takeover. Several unusual small online purchases raise risk level. What time is it at customer location (odd hour for banking)? Have there been any recent SIM card changes or a different inbound caller ID from previous interactions? Monitor account while contacting customer. Customer contacted via . If contact not made within 24 hrs, also attempt via voice call to landline and mobile phone. Block any subsequent high-risk purchases or fund transfer attempts. Customer confirms new user is not authorized before fraudster has opportunity to break out and empty funds. Real-time feedback page 4

5 »Stopping» More Fraud Faster Resource constraints and concern about annoying customers with false positives have traditionally discouraged banks from generating cases for transactions in midrange fraud scorebands. Figure 6: Reaching deeper into scorebands to catch more fraud and minimize customer impact Fraud risk scores Normal call center 850 focus 10 : Additional automation capacity 55 : 1 Figure 7: UK banking group reduces losses with auto-resolution Unrecovered fraud losses 70% Typical false positive : fraud ratios Auto-resolution eliminates both resource and false positive constraints. While an expert analyst is often able to resolve fewer than 20 cases per hour, a virtual analyst can resolve an unlimited number with no staffing increase and while keeping customers happy. By reaching deeper into scorebands, banks catch more fraud. As shown in Figure 6, the incidence of fraud is lower at a score of 450 than it is at a score of 850. Nevertheless, fraud is still present at this level, even with a lower detection rate, so the benefit to detecting fraud in this range is significant. FICO clients across the globe are reaping the benefits of digging deeper: One UK bank that implemented auto-resolution is resolving 250% more fraud cases achieving 30% fewer declines and 95% customer satisfaction with zero FTE increase. Another UK bank has reduced the average number of transactions per fraud found from 5 to 3. A US bank reduced net fraud losses by 75%. Banks vary in how they re assigning this expanded range of fraud cases to human and virtual fraud analysts. Some write case management rules that continue to direct the highest-risk cases to their most experienced analysts, while directing midrange risk cases to auto-resolution. Some prefer to put auto-resolution on the high-risk cases first, since at this level, speed-to-contact and rapid resolution are most crucial for keeping losses down. Fraud loss per case Overall fraud losses 50% 35% Some banks are even opting to deploy auto-resolution across the entire score range, freeing their expert analysts to investigate confirmed fraud and interact with customers on very complex and high-value cases. Best-in-class auto-resolution solutions include easyto-use business rules management and workflow management tools that enable fraud department managers to experiment with different approaches, and quickly capture and compare results metrics. Figure 8: US and UK banks use different auto-resolution strategies both achieve major efficiency gains Cases worked 300% (same FTE) Cases worked 60% ( 30% FTE) page 5

6 »Leveraging» Rapid Feedback for Higher Performance Figure 9: Gathering more fraud information with a smartphone app Another way auto-resolution helps banks stop fraud faster and reduce losses is through the feedback to detection analytics. This feedback comes in quicker with auto-resolution. If the bank is using auto-resolution to probe down into midrange scorebands, there s also more feedback to work with. And if the bank has online and mobile apps that customers can use to quickly confirm multiple transactions, as shown in Figure 9, the volume of data coming back to analytics is even greater. Fraud detection systems can leverage this incoming data in several ways. Fraudulent online authentication attempts, for instance, are highly predictive of account takeovers. But when fraudsters fail authentication, they never get to the transaction step, so fraud detection systems don t generally receive any data. Autoresolution systems, however, can immediately pass data on confirmed fraudulent authentication attempts to detection systems. Incoming auto-resolution data can also be used by detection analytics to self-adjust in real time to changing fraud patterns and customer behaviors. This is a unique capability of FICO Falcon Fraud Manager. As shown in Figure 10, the Falcon system incorporates an adaptive model. This analytic layer contributes a valuable additional perspective because it s focused on and highly responsive to what is happening in the current production environment. All transactions the base model scores above a bank-selected cascade threshold go to the adaptive model for an additional score, which is then blended with the first one. Figure 10: Rapid feedback increases adaptability of detection models Base analytics Adaptive analytic layer Transactional data Profile Scaled profile Model Base score Cascade threshold Model x q i i, p z i l c i, p Rapid feedback from autoresolution accelerates the unique adaptive capabilities of FICO Falcon Fraud Manager, enabling banks to detect emerging fraud schemes and adjust to changing customer behavior faster Virtual analyst Review threshold Confirmed fraud cases for final resolution Blended score Expert analyst Rapid feedback of fraud/no fraud confirmations Adaptive score é ê Fraud/no-fraud tables Updating of variable weights (continuous) Variable evaluation & replacement based on relevancy to production data (frequent intervals) Variable candidate pool page 6

7 The adaptive model uses fraud case resolutions and data to evaluate which data variables are proving most predictive of fraud now and in the recent past (past day, past week, etc.). For example, are the values for the dollar amount variable significantly different between electronics purchase transactions confirmed as fraudulent vs. similar transactions confirmed as legitimate? The adaptive model swaps these currently high-performing variables into its fraud/no fraud tables while swapping out those currently less predictive. It also uses the case dispositions to continuously adjust variable weighting to the changing fraud patterns it s seeing. These adaptive processes improve detection of emerging fraud schemes, so banks can stop them sooner. When combined with other dynamic analytic techniques also built into the Falcon system such as self-calibrating profiles that tune base model weights to changing customer behavior or economic conditions the result is more accurate fraud detection and reduced false positive rates at every scoreband.»increasing» Customer Loyalty and Account Usage Figure 11: Higher revenue auto-resolution results from one bank s debit portfolio Active debit (spend) population + Number of transactions + There is evidence, both from what customers say and what they do, that the experience of a timely, dialog with a virtual fraud analyst builds loyalty. It also increases the value customers feel they re receiving from the bank, and influences their behavior in ways that drive revenue and profit. 10% 1% The most clear cause-and-effect impact on revenue is, of course, at the point of sale. When checkout problems arise, immediate, helpful intervention greatly lessens the embarrassment and inconvenience that customers may experience. It also enables more legitimate high-risk transactions to go through. With the ability to contact customers within 30 seconds to confirm legitimacy, one UK bank has reduced declines by 32%. And when a very high fraud score causes a bank to block or restrict a card, faster resolution reduces the impact on subsequent transactions. Another UK bank is able to contact 79% of its customers within 10 minutes of taking such actions. If the customer confirms the transaction is legitimate, blocks and restrictions are removed instantly. Average ticket value per spend + 23% For online purchases, auto-resolution helps banks avoid losing transaction fees because of failed authentication attempts by legitimate cardholders. For example, if a customer mis-keys his 3D Secure passcode (e.g., Verified by Visa, MasterCard Secure Code), the virtual analyst can let him know immediately that this was the reason for the decline and prompt him to try again. These impacts are increasingly important to banks that are changing the way they measure fraud management performance. A growing number of UK and European banks have expanded their KPIs (key performance indicators) to include not only reducing fraud losses, but increasing spend per card also. Quick resolution can also help boost spend by increasing the customer s confidence about using the card again. Instead of switching to another card or curtailing shopping altogether, the consumer is more likely to make subsequent purchases with the same card. In cases of confirmed fraud, it reduces the odds 1 in 5, according to a 2010 Datamonitor global survey 1 that the consumer will close the account altogether. 1 Consumer-Facing Fraud Prevention Strategies in Payments study, Datamonitor (Ovum), page 7

8 Moreover, because auto-resolution is managing a customer-level experience, a single dialog can confirm status and transactions on multiple cards/accounts. If fraud is confirmed on a debit card, for instance, the customer doesn t have to wonder if a credit card attached to her deposit account has also been compromised; auto-resolution can ease her fears on the spot. As a result, the increased confidence she feels, and its impact on subsequent usage, may also extend beyond a single account, to the full relationship. Though the numbers vary by country, the Datamonitor survey mentioned above found that half of victims and nearly as many non-victims agreed they can do more to protect themselves from fraud. While detecting and preventing fraud will continue to be the banks responsibility, two-way mobile communications that enable consumers to increase their awareness and play a more active role in the process can reduce fraud losses and help bring down the costs of using convenient payment vehicles like credit cards. That feeling of being part of a valuable relationship, based on trust and characterized by good communication is precisely what banks are now working so hard to create with their customers. The beauty of adding auto-resolution to fraud management is that it helps banks rise above obstacles to this goal, such as siloed data stores that have made it difficult to manage fraud interventions at a customer level. At the same time, it provides an ideal opportunity for initiating a dialog that consumers perceive as aimed at their welfare, rather than just trying to sell them something. Combining auto-resolution with advanced adaptive analytic detection FICO offers best-in-class fraud solutions for auto-resolution and real-time detection. When used together, the solutions enable banks to improve fraud management and increase customer loyalty. FICO Fraud Resolution Manager reduces the time required for fraud resolution from hours or days to seconds or minutes. Based on fraud output (i.e., scores, rule results) from a transactional fraud detection system, it performs high-volume, real-time intelligent assessment of fraud cases, identifying the best contact strategy for each customer in each situation. It executes the strategy across one or more channels, managing a customer-level experience, and feeding back data to analytic detection. Combined with the fraud detection capabilities of FICO Falcon Fraud Manager, the feedback loop from auto-resolution delivers powerful benefits. The unique adaptive analytics in the Falcon system can use information from the abundant stream of timely auto-resolution data to adapt fraud risk scoring to changing fraud patterns and customer behaviors. The result is faster detection of emerging fraud schemes, enabling banks to stop them sooner. page 8

9 »Conclusion» Going forward, there s great potential for banks to boost fraud detection by further integrating customer dialog/confirmation into self-learning analytics. There is also an opportunity to build out fraud management dialogs in personalized ways for each customer. Some customers, for example, want more visibility into and control over their accounts on a day-to-day basis. They might appreciate alerts and helpful dialog about balance levels and spending pattern changes (e.g., inflow/ outflow imbalances). They might like timely ideas on how they could use their existing credit more advantageously. Others want to know more only when there s a problem. As consumers become more accustomed to dialog-based services, banks will be able to encourage them to provide detailed preferences and make a range of opt-in choices when opening new accounts. Auto-resolution will use all of this input to shape highly personalized customer experiences, providing many golden moments to grow relationships over the long term. Learn more: Download Insights white papers, which regularly cover fraud innovations and best practices. Subscribe to the FICO Banking Analytics Blog for the latest fraud news, research and ideas. The Insights white paper series provides briefings on research findings and product development directions from FICO. To subscribe, go to For more information North America toll-free International web (0) FICO, Falcon and Make every decision count are trademarks or registered trademarks of Fair Isaac Corporation in the United States and in other countries. Other product and company names herein may be trademarks of their respective owners Fair Isaac Corporation. All rights reserved. 2930WP 09/13 PDF

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