Developing a comprehensive governance framework combining findings from a maturity assessment, ISO standards and sector best practices

Challenge

• No existing framework defining the ideal state and goal of the advanced analytics governance efforts
• Several factors suggesting that a framework to oversee analytics model governance was needed

1. Upcoming regulations
2. Improve collaboration
3. Operational and reputational risks

Solution

• Co-designed a comprehensive ideal governance framework that set a target for the governance and analytics efforts in the organisation
• Framework developed based on previous maturity assessment, ISO standards and external best practices

Business Impact

• Mitigation of operational, financial, legal & reputational risks from analytics solutions through best-in class governance practices
• Standardised and more efficient development practices

Creating an Azure distributed cluster to analyze and assess risk exposure in insurance based on intelligence extracted from contracts

Challenge

• High legal costs due to delayed understanding of contract content are a major cost factor
• Millions of insurance contracts with different wording, exclusion and inclusion clauses
• Difficult to categorize and quantify risk profile of grops of contracts

Solution

• Natural Language Processing solution to analyze contracts
• Contract corpus data lake implementing business rules for contract intelligence risk resolution
• Backend system for underwriters to better formulate future contracts and reduce risk exposure

Business Impact

• Client is able to quickly quantify risk exposure based on contract language
• Underwriters following guidance system resulting in more profitable contracts.
• Reduced legal and operational costs and improved process excellence

Conducted a comprehensive maturity assessment of analytics capabilities, allowing for better future strategic planning and increased transparency

Challenge

• A global insurance company was facing different levels of analytics maturity across its business units and had little visibility on the existing capabilities, tools, standards and project roadmaps

Solution

• Co-developed and conducted a comprehensive analytics maturity assessment by means of a written questionnaire and follow-up interviews.
• Many dimensions were considered, including analytics strategy, design and ethics

Business Impact

• Substantial gains in transparency for the client on its existing capabilities
• Results served as a baseline for future group-wide strategy initiatives regarding analytics, such as strategic workforce planning or analytics governance

Creating automatic reporting tools through data centralisation, eliminating tedious and repetitive tasks anc increasing quality of reports

Challenge

• An insurance company creates quarterly financial reports. They were manually extracting data, exchanging it via emails back and forth for aggregation in Excel and further validation

Solution

• Ingestion of the source data to the data lake and implementation of the pipeline to validate and clean the data.
• UI application was built to streamline the process reporting process by giving access to all parties and allowing modifications if necessary

Impact

• Eliminated the repetitive and tedious manual process, reducing the amount of time needed to create reports
• Improved the quality of reports by reducing the margin of human error

Normalising existing ontology and migrating to a new standardised ontology version

Challenge

• Existing Ontology too large, makes it hard to manage and analyse

Solution

• Normalised existing tables by splitting them into multiple units
• Built toolkits and ontology library to support development of new ontologies
• Conventions and development guidelines
• Performance optimisation and reporting

Impact

• Normalised 3 tables resulting in final ontology of 18 tables (approx. 2.3B rows)
• 30% reduction in total ontology build time
• 60% reduction in disk spillage

Creating a live contract renewal dashboard allowing underwriters and region directors to have a better understanding of trends and current contract status

Challenge

• Create live dashboard and weekly report to track contract renewal progress

Solution

• Built a big data pipeline to track contract status progress and calculate year-to-year metrics
• Developed application to visualise live data
• Created charts enabling easy visualisation of contract renewal status within the market

Impact

• Underwriters are now able to see live data and adjust contract pricing
• High-level automated reports allow region directors to have a better understanding of current trends and statuses of contracts

Conducting a comprehensive maturity assessment of analytics capabilities, allowing for better future strategic planning and increased transparency

Challenge

• A global insurance company was facing different levels of analytics maturity across its business units and had little visibility on the existing capabilities, tools, standards and project roadmaps.

Solution

• Co-developed and conducted a comprehensive analytics maturity assessment by means of a written questionnaire and follow-up interviews.
• Many dimensions were considered, including analytics strategy, design and ethics.

Impact

• Substantial gains in transparency for the client on its existing capabilities.
• Results served as a baseline for future group-wide strategy initiatives regarding analytics, such as strategic workforce planning or analytics governance.

Developing a comprehensive governance framework combining findings from a maturity assessment, ISO standards and sector best practices

Challenge

• No existing framework defining the ideal state and goal of the advanced analytics governance efforts

Solution

• Co-designed a comprehensive ideal governance framework that set a target for the governance and analytics efforts in the organisation
• Framework developed based on previous maturity assessment, ISO standards and external best practices

Impact

• Mitigation of operational, financial, legal & reputational risks from analytics solutions through best-in class governance practices
• Standardised and more efficient development practices

Supporting a strategic initiative to migrate current pipelines and use cases to the Foundry platform

Challenge

• Need to offer company-wide support in the transition from old systems to Foundry
• Multiple teams without prior Foundry knowledge needed to move their pipeline and use cases on the platform

Solution

• Multiple support processes depending on the needs
• Support including: office hours, architecture reviews, pair programming, implementation & handover, code reviews, operational support, etc.

Impact

• Supported strategic migration to Foundry, onboarding new users and setting up data foundations
• Platform usage increased from 2’000 to 5’000 users
• Provided support for 25+ projects
• CoE team grew from 2 to 10+ people in 2 years

Creating an application to present a heatmap of insured value allowing to easily spot overexposed areas and limit financial losses due to natural disasters

Challenge

• Existing struggle to identify overexposed areas, either in terms of total insured value or per given peril

Solution

• Creation of an application to present a heatmap of insured value, either total or per peril, for all granularity levels (country, region, etc.)
• Integrated many filtering options allowing for deeper analysis in certain areas if necessary

Impact

• Client is able to quickly spot overexposed areas and coordinate with underwriters to either stop offering insurance for items in this area, or change the pricing
• Lowering of financial risk in the event of a natural disaster

Problem

• Any quantity varying over time can be represented as a time series: sales numbers, rainfall, stock prices, CO2 emissions, Internet clicks, network traffic, etc. Time series forecasting — the ability to predict the future evolution of a time series— is a key capability in many domains where anticipation is important. Although there exist many models and tools for time series, they are often non-trivial to work with, because they each have their own intricacies and cannot always be used in the same way

Solution

• Darts is our open source library for time series forecasting, attempting to simplify time series processing and forecasting in Python

Impact

• Speed-up the process related to time series forecasting in order to
  – Decrease costs
  – Improve accuracy
  – Reduce manual work

Detecting potential claim fraud among health insurance clients

Challenge

• Undetected fraudulent insurance claims may cause a significant blow to insurance companies’ revenues

Solution

• Implemented fraud scoring KPI’s together with visualisation
• Enabled multiple insurance companies to use the API to validate claims of their customers
• Ensured auditability and idempotency of the results

Impact

• Automated the fraud-flagging process
• Created tools allowing enhanced manual inspection of the most suspicious claims for multiple companies

Developing a climate risk scoring model scalable to the entire insurance portfolio and able to determine climate risk exposure in real time for any locations

Challenge

• Current climate risk scoring model is limited
• Scoring too time-consuming and inability to score for many locations at once
• Model needs to be scaled to the whole insurance portfolio

Solution

• Implemented new model in Big Data environment capable of ingesting high volumes of data
• Used external weather model to predict climate risk score and estimate impact on insurance portfolio

Impact

• High performance model now allowing to evaluate climate risk scores in real time
• Ability to display climate risk scores for any of the more than 2B locations in the insurance portfolio

Developing a centralised solvency and risk assessment model to create risk exposure reports automatically and improve data quality

Challenge

• Difficulty to predict total exposure to financial losses resulting from companies at risk of becoming insolvent
• Current risk reports are generated manually and data quality is not guaranteed

Solution

• Central gathering and processing of the relevant partner data allowing for central risk modelling and data quality checks
• Automatic creation of monthly risk reports based on the curated data

Impact

• Improved risk management capabilities through information centralisation and standardisation
• Improvement in data quality and report generation
• Possibility to create reports dynamically to visualise more aspects than before