March 3rd, 2022

Treat Each Inbound Caller by Their Riskiness

Inbound call centers across industries manage many types of risk beyond high-stakes account takeover. Retailers must guard against callers attempting to return stolen merchandise or activate ill-gotten gift cards. Insurers and healthcare providers must beware callers seeking sensitive consumer information that would enable identity theft. Travel and hospitality businesses must protect loyalty program accounts. Often, the risk drives inbound call centers to take unnecessary caution with legitimate callers.

Inbound call centers face a hard choice. Either they chance undue financial loss by performing an inadequate risk assessment on unidentified callers, or they degrade customer experience and operational efficiency by applying overly stringent protections. These problems will persist as long as inbound call centers struggle to establish caller risk efficiently and effectively.

Assessing for the average caller increases risk

Inbound call centers often struggle to accurately establish caller identity and risk for lack of adequate and accurate signals. More than four out of five[1] call centers rely on agents as a first line of defense. When inbound call centers maintain customer accounts, agents often ask challenge questions (aka, knowledge-based authentication or KBA). Unfortunately, KBA often fails to confirm identity because answers to the most common challenge questions have been breached[2] or unwittingly shared by consumers on social media. Bad actors combine KBA answers with social engineering to gain the trust of call center agents and swindle the organization.

Other alternative signals of caller risk often elude inbound call centers, either because of challenges with accuracy or availability. Comparing caller voice samples against a blocklist will likely only work for caller voices associated with past fraud—not unrecognized fraudsters. Similarly, organizations generally do not exchange past results of risk assessments with other organizations. Attempts at identification via CRM records often fail because consumer data is constantly changing: millions of consumers change their phone carriers (75 million) and numbers (35 million) every year, or call from unknown phone numbers. Only telephone carriers have access to the detailed information on call origin and routing needed to fully assess call trust. Consequently, inbound call centers must treat all callers with the same inefficient, ineffective and suspicious approach.

Businesses and consumers both lose when all callers undergo a uniform risk assessment that determines risk primarily via caller engagement.[3] Bad actors freely gather reconnaissance[4] over dozens of calls to determine risk assessment protocol[5] before executing identity-based fraud, a “very serious issue” for 80% of call centers.[6] Call centers waste between 30 and 60 seconds assessing the risk of legitimate callers before providing service, ballooning average handle time and beginning legitimate interactions with suspicion. Trustworthy callers receive service that is slower and less personalized, degrading their overall customer experience.

Call centers that fail to establish caller risk early and accurately risk dragging down the business’s brand and bottom line. Financial losses mount quickly when bad actors discover easy targets and alert peers to the opportunity. Dissatisfied customers defect to competitors that provide better service. Excessive operating costs sap resources that could be invested in increasing efficiency and improving customer experience.

Let caller risk determine treatment

Forward-thinking organizations overcome these challenges effectively and efficiently by assessing caller risk via analysis of signals produced by the corresponding calls within the telephone network. Speed, reliability and security all increase when call signal analysis primarily drives risk assessment, rather than caller engagement.

Signals from past phone activity, call origin, originating carrier, consumer identity data, and other sources can power an appropriate treatment strategy for each caller and intended action. Past phone activity from billions of calls and dozens of participating organizations yields a robust repository of insight into the riskiness of calling numbers, even numbers that are unknown. Data about call origin and originating carrier — available only to licensed carriers — provides insights that cannot be manipulated by bad actors. Consumer identity data from authoritative sources helps to identify callers using unknown numbers and expedite service.

Callers deemed low- or no-risk via this form of risk assessment receive more efficient and personalized service, improving the overall customer experience. Average handle time goes down, with callers’ needs addressed 30 to 60 seconds sooner than they would if the agent used KBA. False positives decline because callers cannot fail the process due to human error. Trustworthy callers may speak with lower-level agents or qualify for a broader variety of higher-value options in an IVR, reducing agent handle time. When legitimate callers wish to perform higher-risk transactions, they experience additional risk assessment that is proportionate for the task.

Discrepancies and anomalies within the call signal data flags risky callers for more cautious treatment. This risk assessment works even when no customer record exists or even if the calling number has never approached the inbound call center before. Flagging risky callers for additional scrutiny helps to optimize deployment of costly fraud-prevention resources.

How Neustar helps inbound call centers to reduce risk and improve customer experience

As a licensed telephone carrier running the largest caller name service in the U.S., Neustar accesses and analyzes the data needed to assess caller risk efficiently and effectively. Neustar Inbound Authentication uniquely establishes an optimal level of trust for each caller by analyzing proprietary data from the processing of billions of calls, data about carriers and network routing only visible to licensed carriers, and Neustar’s authoritative view of consumer identity.

Completing a risk assessment according to individual caller risk enables inbound call centers to mitigate risk of financial loss, embarrassing data disclosures, customer frustration and operational waste. Basing the process on signals from the call reduces false positives, improving the fraud department’s effectiveness and efficiency. Shortening the risk assessment experience for trustworthy callers and offering more valuable self-serve options improves customer satisfaction by 15%.

Approximately 95% of callers pass inspection by Neustar Inbound Authentication and experience less friction, reducing average call handle time by 20%. This is a boon to both customer experience and operational efficiency. Trustworthy callers qualify for higher-value matters in the IVR, reducing IVR-to-agent transfers by 10%. Only calls showing sufficient risk encounter stepped-up authentication, special routing, limited permissions or closer scrutiny.

By determining caller risk via signals from the call — instead of relying on caller engagement — inbound call centers operate in closer alignment with their risk tolerance. Instead of facing a hard choice between competing business priorities, inbound call centers improve fraud prevention, customer experience and operational efficiency at the same time.

[1] Klie, Leonard. “Contact Center Fraud Sees a COVID-Induced Spike.” DestinationCRM.com. November 30, 2020. https://www.destinationcrm.com/Articles/CRM-Insights/Insight/Contact-Center-Fraud-Sees-a-COVID-Induced-Spike-144078.aspx

[2] “2022 ITRC Annual Data Breach Report.” Identity Theft Resource Center. Accessed February 16, 2022. https://notified.idtheftcenter.org/s/

[3] Inscoe, Shirley. “Improved Customer Experience, Reduced Fraud and Cost: Contact Center Solutions.” Aite-Novarica. December 2020. https://aite-novarica.com/report/improved-customer-experience-reduced-fraud-and-cost-contact-center-solutions.

[4] Tedder, Krista. “Securing the Contact Center.” Javelin Strategy & Research. December 16, 2019. https://www.javelinstrategy.com/coverage-area/securing-contact-center.

[5] Lee, Kevin, Benjamin Kaiser, Jonathan Mayer, and Arvind Narayanan. “An Empirical Study of Wireless Carrier Authentication for SIM Swaps.” Proceedings of the Sixteenth Symposium on Usable Privacy and Security. (2020). https://www.usenix.org/conference/soups2020/presentation/lee

[6] Klie, Leonard. “Contact Center Fraud Sees a COVID-Induced Spike.” DestinationCRM.com. November 30, 2020. https://www.destinationcrm.com/Articles/CRM-Insights/Insight/Contact-Center-Fraud-Sees-a-COVID-Induced-Spike-144078.aspx

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