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To date in insurance, machine intelligence (MI) has yielded returns in areas such as customer analytics and claims processing. Insurers have adopted machine intelligence to process text from contracts, documents, email and other online communications tools more efficiently, and to analyse massive amount of data generated by the digital economy. With these new capabilities, insurers can better understand the unmet consumer needs to better design, assess and distribute protection covers, and extend their reach into new markets.
Many MI approaches require large amounts of high-quality data that are complete, clean and timely to train algorithms to offer enterprise-scale transformative benefits, delivering production-ready data strategy. In certain case, MI solutions could also help fill the data gap, with synthetic data or with algorithm to augment existing data sets. Without these capabilities, the performance of models or algorithms has proven to be slow and costly when compared with existing human-centric processes. If deployed correctly, the models or algorithms can deliver substantial return on investment.
Each data set has different degree to provide value when integrated and assessed for MI Insurance scenario. We see the industry struggle to adopt new data without a clearer understanding of how these insights correlate with actual risk experience - an obvious area of focus for actuarial data science for the years to come
If we pick insurance underwriting for example, supervised learning can complement and eventually streamline certain existing processes. These include smarter triage and routing that may be more efficient than purely relying on current business rules, eg, triage between depths of investigation (full vs. simplified underwriting), accurately waive additional evidence (lab tests, physician statements) or allocate referrals to the right level of underwriting staff (junior underwriter vs. medical officer) for more efficient assessment.
Swiss Re has recently developed data-driven underwriting solutions using Machine Intelligence to help insurers address specific objectives to continually improve the consumer journey. In a recent project, we have delivered AI-driven predictive models that can triage Life & Health underwriting and simplify the consumer journey (for more than 60% of existing customers) using Machine Learning to segment customers based on their risk level. If insurers are still in the data accumulation phase, we enable them with evidence-based research and proxies for underwriting assessment.
To maximise the efficiency, these AI models have also been integrated into Magnum Pure©, our Life & Health automated underwriting solution platform, to orchestrate and utilise both Human and Machine Intelligence at scale. With Magnum Pure©, this expert-backed, AI and machine-learning enabled risk assessment solution can be easily deployed across multiple distribution channels and PoS tools.
We also support insurers on their data-driven transformation across the value chain providing risk insights with pricing analytics, claims analytics and portfolio deep dives (see figure above). As insurers access more (digital) data points from their customers, opportunities exist for them to connect and analyse the potential for offering more customised protection solutions with other external data sets (from their digital partners or open source). We also used Natural Language Understanding techniques to transform unstructured data, mainly from legacy insurer systems or partners, to bring those valuable data/forms back into the MI enabled processes.
In Swiss Re's experience, system design and management often fall short when insurers attempt to implement MI into existing cross-functional processes. Insufficient resources are dedicated to integrating models and algorithms into existing workflows to transform the customer journey. In an interview with Swiss Re Institute, one insurer seeking to eliminate unnecessary underwriting questions said it leveraged banking transaction data to offer accelerated underwriting to prospects. The MI[1]enabled underwriting model performed well in classifying individuals into the relevant segments. The marketing department, however, did not invest (yet) in a propensity-to-buy exercise to take full advantage of the new underwriting system.
“Swiss Re has recently developed data driven underwriting solutions using Machine Intelligence to help insurers address specific objectives to continually improve the consumer journey”
Many other hurdles exist in the industry to scale deployment of MI solutions today, such as limited innovation management and empowerment, slow feedback loops to validate model performance (such as for mortality risk), lack of strategy to acquire and retain data/ partners/talent, lack of long term clarity from regulation on AI applications, the list can go on, but we also see the industry starting to look more strategically at data and Machine Intelligence from board level to operations.
MI viability should not only be assessed on the basis of initial small[1]scale proof-of-concept pilots of models/ algorithms. The criteria to evaluate performance should be more holistic and include integration of direct (development and running - i.e., ML Ops) and indirect (organisational and opportunity) benefits and costs.
While chief data officers and scientists have become common-place at insurers, firm-wide data strategy, part of business priorities, is the key to harvest the full power of data across an organisation. Data strategy also include data quality control and flow cycle (see above) as well as adequate underlying technology to support sustainable and value at scale with MI enabled solutions. System design, deployment plans and success criteria should focus on business workflow context, decision support and enterprise productivity. Regulatory risks regarding tech-linked innovation in insurance, in particular around data privacy and use (i.e., fairness and transparency), also need to be considered. A good example is the Veritas initiative driven by MAS in Singapore, with Swiss Re actively participating, to address implementation challenges in the responsible use of Artificial Intelligence and data analytics.
Most importantly, an MI project also needs clear and understandable communication across all facets, to secure senior management buy-in. To successfully transform their enterprises with MI-enabled technology, we recommend that insurers focus on the salient, non-model characteristics of end-to-end enterprise deployment such as data strategy, culture change including upskilling all workforce, prioritisation by business value of MI use cases, integrated data-driven decision process and tools, just to name a few.
To maximise the efficiency, these AI models have also been integrated into Magnum Pure©, our Life & Health automated underwriting solution platform, to orchestrate and utilise both Human and Machine Intelligence at scale. With Magnum Pure©, this expert-backed, AI and machine-learning enabled risk assessment solution can be easily deployed across multiple distribution channels and PoS tools.
We also support insurers on their data-driven transformation across the value chain providing risk insights with pricing analytics, claims analytics and portfolio deep dives (see figure above). As insurers access more (digital) data points from their customers, opportunities exist for them to connect and analyse the potential for offering more customised protection solutions with other external data sets (from their digital partners or open source). We also used Natural Language Understanding techniques to transform unstructured data, mainly from legacy insurer systems or partners, to bring those valuable data/forms back into the MI enabled processes.
In Swiss Re's experience, system design and management often fall short when insurers attempt to implement MI into existing cross-functional processes. Insufficient resources are dedicated to integrating models and algorithms into existing workflows to transform the customer journey. In an interview with Swiss Re Institute, one insurer seeking to eliminate unnecessary underwriting questions said it leveraged banking transaction data to offer accelerated underwriting to prospects. The MI[1]enabled underwriting model performed well in classifying individuals into the relevant segments. The marketing department, however, did not invest (yet) in a propensity-to-buy exercise to take full advantage of the new underwriting system.
“Swiss Re has recently developed data driven underwriting solutions using Machine Intelligence to help insurers address specific objectives to continually improve the consumer journey”
Many other hurdles exist in the industry to scale deployment of MI solutions today, such as limited innovation management and empowerment, slow feedback loops to validate model performance (such as for mortality risk), lack of strategy to acquire and retain data/ partners/talent, lack of long term clarity from regulation on AI applications, the list can go on, but we also see the industry starting to look more strategically at data and Machine Intelligence from board level to operations.
MI viability should not only be assessed on the basis of initial small[1]scale proof-of-concept pilots of models/ algorithms. The criteria to evaluate performance should be more holistic and include integration of direct (development and running - i.e., ML Ops) and indirect (organisational and opportunity) benefits and costs.
While chief data officers and scientists have become common-place at insurers, firm-wide data strategy, part of business priorities, is the key to harvest the full power of data across an organisation. Data strategy also include data quality control and flow cycle (see above) as well as adequate underlying technology to support sustainable and value at scale with MI enabled solutions. System design, deployment plans and success criteria should focus on business workflow context, decision support and enterprise productivity. Regulatory risks regarding tech-linked innovation in insurance, in particular around data privacy and use (i.e., fairness and transparency), also need to be considered. A good example is the Veritas initiative driven by MAS in Singapore, with Swiss Re actively participating, to address implementation challenges in the responsible use of Artificial Intelligence and data analytics.
Most importantly, an MI project also needs clear and understandable communication across all facets, to secure senior management buy-in. To successfully transform their enterprises with MI-enabled technology, we recommend that insurers focus on the salient, non-model characteristics of end-to-end enterprise deployment such as data strategy, culture change including upskilling all workforce, prioritisation by business value of MI use cases, integrated data-driven decision process and tools, just to name a few. 