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

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

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 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

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