How has Artificial Intelligence (AI) altered the human experience? There are a million ways to answer this, but to put it succinctly, the ever-evolving technology has made our lives simpler, smarter and safer. Natural Language Processing (NLP), a subset of AI, is the driving force behind myriad applications enhancing the man-machine interaction (think virtual assistants andchatbots). NLP tools are being widely adopted across industries to improve efficiency and customer experience. The burgeoning insurance sector is no exception.
In a data-driven industry, insurance companies commonly find themselves drowning in voluminous documents and facing operational challenges. Hence, they often turn to Business Process Outsourcing (BPO) to free up internal resources and meet sky-high customer expectations. NLP benefits both insurers and their insurance BPO partners. Enabling computers to comprehend and interpret human language, the technology facilitates the extraction of valuable insights from text-based data, streamlining insurance processes and mitigating fraud. Let’s examine how it can help companies stay on top of their game.
adoption benefits — efficiency, accuracy and more
automated underwriting: Underwriters, who assess the potential risks of providing insurance coverage and determine premiums, are often a burdened lot. NLP takes the load off their shoulders by processing unstructured text data from hundreds of claim applications and automatically mining critical information swiftly. This allows underwriters to dedicate time to complex cases which can benefit from human judgement.
chatbots for better customer experience: NLP-powered chatbots offer instant support to policyholders, saving them the trouble of enduring lengthy wait times. Be it a query about claim status or premium payment, they provide round-the-clock, personalised assistance. They can also recommend suitable policy coverage and remind customers about the policy renewal date, potentially reducing churn.
claims management and processing: Despite technological advancements, many aspects of claims management still rely on manual processes, which are slow and leave scope for errors. NLP expedites claims processing by extracting pertinent information from claim forms and medical/police records. These algorithms also evaluate claim validity and calculate appropriate settlements. While speedier resolution leaves the customer smiling, the insurer benefits from better operational efficiency and lower administrative costs.
fraud detection and prevention: In the US alone, fraudulent claims cost insurers and policyholders a whopping $308 billion every year, as per The Coalition Against Insurance Fraud. For example, a customer may inflate a stolen laptop’s cost or lie about his car being involved in an accident at a particular time. How can insurers tackle this? By deploying NLP, a powerful weapon which can recognise inconsistencies like exaggerated claims or discrepancies in entities like names, dates, locations and monetary amounts.
risk assessment: With its speed and accuracy, NLP is transforming how insurers assess potential risks. As per a Chartis Research survey on the adoption of AI methods by risk/compliance professionals, 37% of the respondents said NLP was either a core component or in extensive use at their workplace. While traditional risk assessment methods might rely on historical data, NLP can assess real-time and unstructured data from IoT devices, news articles, social media, etc. This can help insurers make more informed decisions.
best practices
monitor/upgrade data quality: It’s said that AI is as good as the data it is fed. Unfortunately, in the insurance industry, data is often found in disparate silos. Hence, it’s essential for insurers to implement data cleansing and validation processes to create cohesive datasets which NLP algorithms can analyse. Making real-time data feeds accessible to NLP applications will go a long way in ensuring efficient underwriting and fraud detection.
regularly audit NLP models’ performance: Institutions must not stop at adoption. Periodic testing of NLP models is vital to determine the reliability and quality of the results produced by them. By employing evaluation metrics, stakeholders can identify areas of improvement and fine/tune or retrain models.
ensure regulatory compliance: Digitisation of any industry comes with its set of challenges pertaining to data privacy and regulatory adherence, and the insurance landscape is no different. Insurers must ensure NLP models are used in compliance with data protection laws. Deploying bias-free and transparent models will not only protect sensitive data but also foster trust in policyholders.
upskill employees: Successful integration of NLP in insurance requires more than technology. What companies need are skilled, AI-ready employees. They should invest in periodic training programmes to help staff work effectively with NLP systems. Closing the skill gap will enable insurers to fully tap the technology, which in turn will drive business growth.
conclusion
NLP has immense potential to transform the entire insurance value chain and hugely benefit customers. With the sector embracing innovation like never before, NLP’s role is likely to become increasingly vital. However, successful implementation requires careful consideration of the associated challenges. Staying vigilant, investing in training/development and collaborating with AI experts can help insurance companies overcome them and stay ahead of the curve.
how can Infosys BPM help?
Infosys BPM’s Insurance BPM Services offers a well-established practice, collaborating with over 45 insurance providers. We work closely with our clients to not only reduce their operational costs but also to spearhead their business transformation initiatives. NLP systems are playing a pivotal role in transforming insurance processes. With Infosys BPM’s AI-first platform, you can leverage the generative AI evolution to re-imagine your business operations and exceed customer expectations.
