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How European Insurer Tryg Improved Customer Service with a Language Neutrality Engine

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How European Insurer Tryg Improved Customer Service with a Language Neutrality Engine Case Study GENERATING INSURANCE IMPACT How European insurer Tryg improved customer service with a language neutrality engine Client Solution Business impact Tryg, the second-largest general insurer in Genpact implemented a language neutrality Language neutralization and real-time Scandinavia, with 3,400 employees and engine with an automated glossary and language translation enabled Tryg to 2.8 million customers in Denmark, Norway standard scripts that reduced response time deliver superior customer service while and Sweden optimizing process efficiency. Tryg can now service a significant portion of Business need addressed operations more effectively and from a Optimized claims and policy central hub administration, reduced costs, and enhanced customer satisfaction through more streamlined communication DESIGN • TRANSFORM • RUN Tryg, one of the largest general insurance companies in Scandinavia was on a mission to identify opportunities for process harmonization, and enhance customer experience, while dealing with a language barrier that came from servicing countries with different languages. Business challenges • Delays in translation and interpretation led to slower response times Given the need to cater to Scandinavian • Lack of process clarity, and difficulty customers, the insurer struggled to optimize communicating with customers, led to customer communication. Tryg’s primary customer support team received customer correspondence inefficiency as well as inconvenienced customers in local languages (Norwegian and Danish), which • Disparate, non-standard procedures lead to a high needed to be translated, categorized, and assigned cost of operations and inefficient non-customer- to over 14 different departments within the centric processes company. The language barrier presented a huge • Issues of coordination and varied, non-standard challenge for the insurer. Key issues included: output created unnecessary risks • All the operations were infused with local • Customers could file first notice of loss (FNOL) languages, which limited the productivity of only through a voice-based customer service operations teams channel, which necessitated live interaction and Web register claim – notification process, motor claim t Start n a m i a l Claiment update C / claims d e information on r u www.abcd.com s n I Application Private/Commercial Open application e Pull up the pdf Verify/ t to retrieve Identify policy a Trance policy r s claim search for o Register the Enter date incoming claims e number and other p Application3 # and claim Y notification policy r claim in CMS of loss from information o information web-unregistered document number C Others No, Team member to leave Application2 a note for missing details Update the claim Update the code Update Assign claim to Select type Update place number in PMS for the type insured and Create reserve the assessor of coverage of loss and move the Genpact claims processor – Noida India of loss incident details or handler document Inform the handler or assessor for next steps in standard template Handel the Communicate claim for for missing Stop Yes, add the details on claim settlement/ information via nearshore deny phone Claims handing Figure 1: Process flow in FNOL registration GENPACT | Case Study | 2 led to communication breakdowns in addition to from end customers, which were not part of the longer turnaround cycles, lower productivity, and tool database. These were centrally captured by the higher claim-processing costs manager and sent to the client for an exact meaning to catch the essence of the statement. At periodic Genpact solution intervals, these were then uploaded on the tool to continuously improve translation accuracy. Genpact implemented a language neutrality engine composed of three different layers. Continuous improvement via machine learning. Genpact’s language neutrality engine functions on The first layer included cheatsheets and glossaries two parameters: a rule-based engine and machine for routine and templatized operations, which would learning capabilities. Machine learning enables cover the bulk of claims and policy administration the tool to become smarter each time it translates customer interactions. This glossary is used by Tryg repetitive words and phrases. To ensure correct during employee inductions and process training contextualization and expedite this learning, to shorten the learning curve. The second layer Genpact analyzed transaction data over several included implementing a language translation tool, months prior to implementation. This data was Systran, for consistent and speedy translation. The then compared to the existing database and all third layer used “smart scoping” to identify which processes could leverage the language translation additional words/phrases were translated and solution and be serviced from any location. During added to the tool with the help of bilingual experts. this step, Genpact analyzed processes for language Genpact also brought in onsite language experts to dependency, which provided the foundation of support interpretation for complex cases. the overall solution. Genpact also implemented a web-based customer service channel to facilitate Business impact effective claims routing during the FNOL process. Genpact’s language neutrality engine enabled Each process of the FNOL claims function was Tryg to address language complexities and documented through a process-flow diagram to improve customer experience. Specifically, Tryg reflect the level of language dependency. Processes realized substantial cost and process efficiency, as were evaluated and categorized for input received, significant operations can now be serviced from structured processes, non-structured processes and one, centralized location. Real-time translation variance in input received. This resulted in: helped this operations center deliver enhanced customer service and technical support while Higher translation accuracy. While in production, process standardization increased the coverage it the team captured incoming free text slang phrases could provide to the business as a whole. GENPACT | Case Study | 3 About Genpact Genpact (NYSE: G) is a global professional services firm focused on delivering digital transformation for our clients, putting digital and data to work to create competitive advantage. We do this by integrating lean principles, design thinking, analytics, and digital technologies with domain and industry expertise to deliver disruptive business outcomes – an approach called Lean DigitalSM. We deliver value to our clients through digital-led, domain-enabled solutions that drive innovation, and digital-enabled intelligent operations that design, transform, and run clients’ operations. For two decades we have been generating impact for clients including the Fortune Global 500, employing 77,000+ people in 20+ countries, with key offices in New York City, Palo Alto, London, and Delhi. For additional information, contact, [email protected] and visit, www.genpact.com/what-we-do/industries/insurance Follow Genpact on Twitter, Facebook, LinkedIn, and YouTube. © 2017 Copyright Genpact. All Rights Reserved. 94320_CS_HowEuropeanInsurerTrygImproved_US_040617.
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