Data Science, AI, and Advanced Analytics United States

Data Analytics for Insurance and Actuarial Science Training Course

Insurance markets now run on granular policy, claims, telematics, and behavioral data, yet many actuarial and underwriting teams still rely on fragmented spreadsheets and legacy reports that cannot keep pace with emerging risks and pricing pressure. Modern practices in data analytics for insurance and actuarial science use predictive modeling, generalized linear models (GLMs), and portfolio risk techniques aligned with frameworks such as Solvency II internal model principles and IFRS 17-style liability measurement to produce defensible, auditable decisions. At the same time, AI-driven fraud detection engines, automation in claims workflows, and insurtech platforms generate new data streams that you must interpret coherently.

Data analytics for insurance and actuarial science training is a structured learning program that applies analytics methods to pricing, reserving, underwriting, and portfolio management using insurance-specific data. It enables professionals to build rating models, analyse claims behavior, and communicate model results to decision-makers. In practical terms, this course gives you the skills to design loss triangles, implement GLM-based pricing models, construct risk dashboards, and prepare technical memos that stand up to scrutiny from chief actuaries, risk committees, and external stakeholders. Designed for actuarial analysts, pricing specialists, insurance data scientists, and underwriting managers, the program builds your capability to translate complex datasets into clear, actionable insight, so you improve technical decisions while supporting strategic, financially sound growth.

Duration
10 Days
Duration
Certificate
Certificate
Included
Delivery
Instructor-Led
Delivery
Level
Foundation To Intermediate
Level
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Live Online Training

Join from anywhere with interactive virtual sessions

Starts
Ends
Mon - Fri (10 Days)
USD 1,700
Starts
Ends
Mon - Fri (10 Days)
USD 1,700
Starts
Ends
Weekend (8 Wks)
USD 1,700
Starts
Ends
Mon - Fri (10 Days)
USD 1,700
Starts
Ends
Weekend (8 Wks)
USD 1,700
Starts
Ends
Mon - Fri (10 Days)
USD 1,700
Starts
Ends
Mon - Fri (10 Days)
USD 1,700

Classroom Training

In-person sessions at premier locations

Nairobi Kenya
Mon - Fri
10 Days
USD 3,200
Kigali Rwanda
Mon - Fri
10 Days
USD 3,800
Dubai United Arab Emirates (UAE)
Mon - Fri
10 Days
USD 8,200
Addis Ababa Ethiopia
Mon - Fri
10 Days
USD 4,900
Customized Content
Team Training
Flexible Dates

In-person training at our premier venues — pick a city and date that works for you.

Location Duration Fee Language
Nairobi, Kenya Mon - Fri (10 Days) USD 3,200 English See dates & reserve →
Kigali, Rwanda Mon - Fri (10 Days) USD 3,800 English See dates & reserve →
Dubai, United Arab Emirates (UAE) Mon - Fri (10 Days) USD 8,200 English See dates & reserve →
Addis Ababa, Ethiopia Mon - Fri (10 Days) USD 4,900 English See dates & reserve →
Abuja, Nigeria Mon - Fri (10 Days) USD 5,600 English See dates & reserve →
Zanzibar, Tanzania Mon - Fri (10 Days) USD 4,800 English See dates & reserve →
Mombasa, Kenya Mon - Fri (10 Days) USD 3,400 English See dates & reserve →
Cape Town, South Africa Mon - Fri (10 Days) USD 7,800 English See dates & reserve →
Johannesburg, South Africa Mon - Fri (10 Days) USD 7,000 English See dates & reserve →
Kampala, Uganda Mon - Fri (10 Days) USD 3,800 English See dates & reserve →
Pretoria, South Africa Mon - Fri (10 Days) USD 6,600 English See dates & reserve →
Lagos, Nigeria Mon - Fri (10 Days) USD 5,000 English See dates & reserve →
Arusha, Tanzania Mon - Fri (10 Days) USD 4,000 English See dates & reserve →
Dar es Salaam, Tanzania Mon - Fri (10 Days) USD 3,800 English See dates & reserve →
Accra, Ghana Mon - Fri (10 Days) USD 7,600 English See dates & reserve →
Kisumu, Kenya Mon - Fri (10 Days) USD 3,200 English See dates & reserve →
Nakuru, Kenya Mon - Fri (10 Days) USD 3,200 English See dates & reserve →
Naivasha, Kenya Mon - Fri (10 Days) USD 3,400 English See dates & reserve →

Live, instructor-led sessions you can join from anywhere — pick the next start date below.

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DAS-34 Mon - Fri (10 Days) USD 1,700 Reserve my seat → Reserve team seats →
DAS-34 Mon - Fri (10 Days) USD 1,700 Reserve my seat → Reserve team seats →
DAS-34 Weekend (8 Weeks) USD 1,700 Reserve my seat → Reserve team seats →
DAS-34 Mon - Fri (10 Days) USD 1,700 Reserve my seat → Reserve team seats →
DAS-34 Weekend (8 Weeks) USD 1,700 Reserve my seat → Reserve team seats →
DAS-34 Mon - Fri (10 Days) USD 1,700 Reserve my seat → Reserve team seats →
DAS-34 Mon - Fri (10 Days) USD 1,700 Reserve my seat → Reserve team seats →

Our instructor comes to your office — same curriculum and accredited certificate, with case studies built around the work your team actually does.

Team Training

Train your entire team together in a familiar environment for better collaboration

Fully Customized

Content tailored to your industry, tools, and specific business challenges

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About the Course

Insurers and actuarial teams face mounting pressure to justify pricing, reserving, and capital allocation decisions with robust, transparent analytics rather than opaque judgement. You need to demonstrate specific capabilities such as building loss development triangles, calibrating GLM pricing models, segmenting risk pools, constructing claims frequency–severity analyses, and producing capital adequacy views consistent with enterprise risk management frameworks like ISO 31000 and principles used in Solvency II internal models. Without structured data analytics for insurance and actuarial science training, teams often struggle to reconcile data from policy admin systems, claims platforms, and external sources, leading to inconsistent rating, reserving swings, and credibility challenges with leadership.

This 10-day program turns scattered technical knowledge into a coherent, end-to-end analytics workflow tailored to insurance. You will work through the full cycle: extracting and cleaning policy and claims data using SQL and Excel, performing exploratory analysis with visualization tools, designing and fitting GLM-based rating structures, applying loss reserving techniques to run-off triangles, and building portfolio risk dashboards. You will also be introduced to predictive analytics methods such as gradient boosting and random forests for underwriting and fraud detection, as well as exposure-based pricing scenarios using telematics and IoT data. In simple terms, this course teaches you how to manage insurance data, build practical actuarial models, and explain results in business language. You will practice data preparation, GLM modeling, and reserving calculations hands-on, while techniques like advanced machine learning and insurtech architectures are covered at an overview level so you know when and how to engage specialists.


Target Audience

This data analytics for insurance and actuarial science training is tailored for professionals who work with insurance data and need to produce credible pricing, reserving, and risk insights that influence technical and strategic decisions.

  • Actuarial Analyst responsible for pricing studies and loss reserving projections
  • Pricing Actuary managing rating structures and portfolio risk segmentation
  • Insurance Data Scientist building predictive models for underwriting and claims
  • Underwriting Manager overseeing risk selection and rate adequacy reviews
  • Claims Analytics Specialist analysing frequency, severity, and settlement patterns
  • Risk Management Actuary supporting capital modeling and stress testing analyses
  • Product Development Actuary designing new insurance products and rating plans
  • Reinsurance Analyst evaluating treaty performance and retention structures
  • Financial Planning & Analysis (FP&A) Analyst supporting insurance profitability reporting
  • Business Intelligence Developer creating insurance dashboards for actuarial and underwriting teams

Course Objectives

This course equips you to design, execute, and measure data analytics for insurance and actuarial science initiatives that improve pricing adequacy, strengthen reserving discipline, and support risk-informed strategic decisions.

  • Analyze policy and claims datasets using SQL and Excel to build insurance-ready data tables.
  • Define key insurance metrics such as loss ratio, pure premium, and ultimate loss using standardized actuarial formulas.
  • Develop GLM-based pricing models incorporating rating factors, exposure measures, and credibility adjustments.
  • Design loss development triangles and calculate IBNR reserves using chain-ladder and Bornhuetter-Ferguson methods.
  • Implement predictive analytics techniques, including logistic regression and random forests, for underwriting and fraud detection.
  • Evaluate model performance with lift charts, ROC curves, and out-of-sample validation specific to insurance portfolios.
  • Map analytics outputs into risk dashboards aligned with ISO 31000 enterprise risk management principles.
  • Synthesize model findings into actuarial reports and presentations that support rate reviews and risk committee decisions.

Requirements & Prerequisites

To benefit fully from this data analytics for insurance and actuarial science training, you should have basic proficiency with spreadsheets, comfort with numerical analysis, and foundational familiarity with insurance concepts such as premiums, claims, loss ratios, and reserves. Prior exposure to probability, statistics, or actuarial exams is helpful but not mandatory at the foundation level. You will use Excel and optionally a statistical tool such as R or Python during exercises; introductory guidance and templates will be provided, so no prior coding experience is required. Please bring or have access to a laptop capable of running spreadsheet software and a modern browser to work with datasets, visualization tools, and online notebooks during the course.


Professional and Organizational Impact

When you lead data analytics for insurance and actuarial science with credible data and practical strategies, you become a trusted driver of technical soundness and portfolio profitability.

  • Build confidence executing pricing, reserving, and risk analytics on real insurance data.
  • Gain fluency with GLM pricing, loss triangles, and portfolio risk dashboards.
  • Strengthen your ability to interpret model outputs for actuaries and underwriters.
  • Enhance your credibility when defending methods to chief actuaries and risk committees.
  • Develop hands-on experience with SQL, Excel, and basic R or Python workflows.
  • Position yourself for advanced actuarial, pricing, or insurance data science roles.
  • Expand your capacity to partner with product, claims, and finance on data-driven decisions.

Organizations that embed data analytics for insurance and actuarial science excellence into pricing, underwriting, and reserving processes reduce volatility, mitigate risk, and build durable competitive advantage.

  • Improve pricing adequacy and rate consistency across products and segments.
  • Reduce reserving volatility through structured loss development and IBNR analytics.
  • Strengthen capital and reinsurance decisions with portfolio-level risk insights.
  • Enhance fraud detection and claims triage using predictive modeling approaches.
  • Increase transparency in model assumptions, improving regulatory and audit readiness.
  • Accelerate rate review cycles through standardized data pipelines and templates.
  • Support profitable product innovation with evidence-based segment and coverage analytics.
  • Elevate board and leadership confidence through clear, data-driven risk reporting.

Training Methodology

This is a practical, outcome-driven course designed to turn insurance analytics aspirations into measurable action and credible actuarial reporting.

Methodology includes:

  • Hands-on calculations of loss ratios, pure premiums, and ultimate losses using real policy–claims datasets.
  • Scenario simulations of rate changes and underwriting strategies on portfolio profitability and combined ratio.
  • Structured diagnostics of pricing and reserving practices against ISO 31000-aligned risk management checklists.
  • Stakeholder mapping of actuarial, underwriting, claims, finance, and risk oversight reporting lines for models.
  • Case study analysis from personal lines, commercial lines, health, and reinsurance sectors focusing on analytics use.
  • Group workshop to build an end-to-end pricing and reserving workbook under specified data and time constraints.
  • Reflection exercise comparing your current analytics workflow to benchmark insurance data science lifecycles.

Upcoming Sessions

Next available dates worldwide

Virtual

(Zoom) Training
USD 1,700
29th Jun-10th Jul 2026

Nairobi

Kenya
USD 2,900
29th Jun-10th Jul 2026

Kigali

Rwanda
USD 3,800
22nd Jun-3rd Jul 2026

Dubai

United Arab Emirates (UAE)
USD 7,800
6th Jul-17th Jul 2026

Addis Ababa

Ethiopia
USD 4,900
15th Jun-26th Jun 2026

Abuja

Nigeria
USD 5,600
22nd Jun-3rd Jul 2026

Zanzibar

Tanzania
USD 4,300
29th Jun-10th Jul 2026

Mombasa

Kenya
USD 3,200
29th Jun-10th Jul 2026

Cape Town

South Africa
USD 7,500
22nd Jun-3rd Jul 2026

Johannesburg

South Africa
USD 6,000
29th Jun-10th Jul 2026

Kampala

Uganda
USD 3,700
15th Jun-26th Jun 2026

Pretoria

South Africa
USD 5,900
22nd Jun-3rd Jul 2026

Lagos

Nigeria
USD 5,000
27th Jul-7th Aug 2026

Certification

Recognized credentials that advance your career

Participants who complete the Data Analytics for Insurance and Actuarial Science Training Program earn a Trainingcred Certificate of Achievement, demonstrating professional competence and alignment with global standards in learning and development.

NITA Accredited

Accredited by the National Industrial Training Authority, ensuring programs meet nationally recognized standards of quality and relevance.

CPD Certified

Recognized by the CPD Certification Service, ensuring every program meets internationally benchmarked standards of professional excellence.

Why this course earns its place on your CV

Accredited training, practitioner trainers, and peers on the same career track — the three things real expertise is built on.

Skills Relevance

  • Master predictive modeling techniques crucial for modern insurance risks.
  • Transform data into actionable insights with cutting-edge analytics tools.
  • Learn from real-world case studies in insurance and actuarial contexts.

Expert Delivery

  • Courses taught by industry leaders with decades of actuarial experience.
  • Benefit from guest lectures by top data scientists in the insurance sector.
  • Interactive sessions ensure you apply concepts to actual insurance scenarios.

Career Advancement

  • Enhance your resume with skills in high demand across financial sectors.
  • Prepare for leadership roles in insurance with advanced analytics expertise.
  • Gain exclusive access to our job placement network in insurance analytics.

Real Results from Real Professionals

Thousands of professionals have transformed their careers through our training programs. Now, it's your turn.

Frequently Asked Questions

Got questions? We've gathered the answers to common queries to help you feel confident and informed.

Who else has attended this training course?

Join global leaders and experts from top-tier organizations who have already benefited from this training. Here are just a few of our past participants:

Designation Organization
Manager, Administration, and Benefits Reserve Bank of Malawi, MALAWI
Statistician Allied Insurance Company, Maldives
ACTUARIAL ASSISTANT KENYA REINSURANCE COOPERATION, Kenya
ACTUARIAL ASSISTANT KENYA REINSURANCE CORPORATION, Kenya
Practitioner NIC Insurance, TANZANIA, UNITED REPUBLIC OF
ACTUARIAL ASSISTANT KENYA REINSURANCE CORPORATION, Kenya

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Join these industry leaders and take the next step in your career.

You will gain hands-on experience with Python and SQL for data manipulation, and use specialized libraries like Scikit-Learn for predictive modeling. You will also build actuarial reserving templates and IFRS 17 reporting dashboards using Power BI and Excel.
Yes, the course is designed for a foundation-to-intermediate level. We provide pre-written scripts and templates in Python and R, focusing on how to interpret and apply the code to actuarial problems rather than building software from scratch.
A dedicated module covers the technical implementation of IFRS 17, including calculating the Contractual Service Margin (CSM) and Risk Adjustment. You will practice building the data workflows required to move from legacy reporting to the new Building Block Approach.
Absolutely. You will explore how to process high-frequency sensor data for usage-based insurance (UBI) and learn to integrate this data into traditional GLM pricing structures for more accurate risk scoring.
You should have a basic understanding of insurance operations and statistics. While no advanced programming knowledge is required, a high level of comfort with Excel and a willingness to engage with data science tools like Python is essential.

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Barbours
Bank of Rwanda
RFA
Dahabshil Bank
Dorcas Aid
Finn Church Aid
KCB Foundation
Ministry of Education Saudi Arabia
NSSF Uganda
RBA
Reserve Bank of Malawi
WASREB Kenya
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