AI-based Risk Scoring & Recommendations in Commercial Underwriting

Artificial intelligence in insurance is used to be about customer experience - 58%, process optimization - 43%, and product innovation - 19%. But that's not the utmost and not the IT team domain. Board members should invest to achieve a deep understanding of AI-related technologies, explore hypothesis-driven scripts, initiate pilots and proof-of-concept projects to bring value for certain business lines.

Are you ready?

  • Find 3 types of unexpected business objectives the insurance organization can achieve by deploying AI.
  • Why ones fail while others gain a competitive advantage using AI in a highly competitive insurance market? - we’ll find the difference.
  • 5 must-answer questions to identify if any insurance process adequate for an AI-fueled organization.
  • Do you use a check-list to find the right partners to build AI ecosystem? - we’ll share our own.
  • Get a model of benchmarking study of the AI implementation estimation in the dependance of the deployment script.
  • Ready to answer what's the right way to start — by small steps or go all-in?
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Prerequisites
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No prerequisites for AI-based Risk Scoring & Recommendations in Commercial Underwriting.
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Insurance organizations representatives

  • CEO, CIO, CTO
  • Product managers, Senior product managers
  • Managing staff (project managers, delivery managers, and others)

Training program

1
Introduction, definition and value for the Insurance
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Scope of AI value
  • Business digitalization. Data informed, data-driven, and data-centric businesses. Paradigm shift.
Definitions in case studies based on the insurance segment
  • Yet another trivia: Data Science, Artificial Intelligence, and Machine Learning, Data Mining, Data Engineering. Experiencing a data set: supervised, unsupervised, and semi-supervised learning. Types of tasks: regression, classification, clustering. Case studies in retail, travel and hospitality, manufacturing, healthcare, energy.
Machine learning in underwriting
  • Decision-making improvement: AI for automation, AI for informing. Case studies in underwriting: submission, segmentation, assignment, risk assessment, coverage recommendation.
2
Data-centric Culture for the Insurance company
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Delivering AI Value
  • Challenges and engineering countermeasures. Strategy, technology and operations.
Stages of technology adoption
  • Usage of common approach for solution development in AL/ML technologies adoption. Proof of Concept as “painless” solution for value evaluation and feasibility. Further steps.
Data Policies as main requirements for supporting of solutions
  • How integration of data-driven analytics and data-based products are made to support proven solutions. Requirements of MVP implementation and solution support.
Data-driven transformations of business processes for the Insurance
  • How new policies change processes inside the company. Roles for a data-driven company. Transformations of a current roles.
3
Delivery specifics for the Insurance company
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Options and trade-offs
  • AI-software as a service (SaaS) and turnkey solutions, Cloud AI APIs; AI platform as a service, fully-managed AI workflow; building custom AI algorithms.
From PoC to MVP
  • Cross-industry standard process for data mining methodology adoption.
Avoiding caveats in production
  • Managing machine learning reproducibility, data pipeline automation, machine learning project management.
Comparison of solutions for AI/ML product
  • Solutions for Development, Deployment and Support of AI products, their pros and cons with blueprint for estimating the cost.
4
Quality Assuarance
see details
Known issues
  • False negative and False Positive - who has to take the risks and how
  • Legacy systems integration - RPA/Intelligent Automation (AI)
  • Not enough or insufficient data quality
  • HIPAA vs GDPR
Real Q&A
  • Your questions

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Select location

When

Number of participants

1
2
3
4
5+

Total price

${{commonPriceString}}

The price rises closer to the training date. Take a minute to buy now!

From - To

Number of Delegates

The course could be tailored to suit your needs and objectives. It can also be delivered on your premises if preferred.

Price

${{privatePriceString}}

Special price

From - To

Number of Delegates

The course could be tailored to suit your needs and objectives. It can also be delivered on your premises if preferred.

Price

${{privatePriceString}}

Special price

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Frequently asked questions

Did not find the answer to your question? Drop us a line at training@altoros.com

What are the prerequisites?
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No prerequisities for AI-based Risk Scoring & Recommendations in Commercial Underwriting.

What are the available payment options?
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You can pay via all major credit cards (including Visa, American Express, MasterCard, Discover, and more) or PayPal. If you would like to get an invoice for your company to pay for this training, please email to training@altoros.com and provide us with the following info: Name of your Company/Division which you would like to be invoiced; Name of the person the invoice should be addressed to; Mailing address; Purchase order # to put on the invoice (if required by your company).

Do you offer group discounts?
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Yes, we also offer a discount for groups of 5 or more people who register together.

I bought a ticket but occasionally should miss training.
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Please, email to training@altoros.com. Our team will provide you information about the nearest date to your location.

What services do you offer after the training?
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We'd be glad to provide you with: Post-training support from our trainers to cover the questions that you or your team might have; Advanced training options or classes on a different domain to widen your expertise and technical skillset (Kubernetes, Cloud Foundry, Artificial Intelligence / Machine Learning); Customized consulting services for project delivery.

Do you offer discounts for individuals?
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Yes, we offer discounts of up to 10% off for early birds who register for the training in advance. Each discount tier has a limited number of seats and all of our discounts are available in limited quantities. Once we sell out of our discounted seats, we move to full price.

What size are the groups?
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The standard group size is 8-10 people. Small class sizes provide students unparalleled access to instructors, who are seasoned industry veterans with experience building and deploying full-scale AI solutions. Note: The class is contingent upon having 7+ attendees. If there aren’t enough students, we will offer you to attend the class in a different location or date. That is also the reason why we ask not to buy flight tickets before the class is confirmed.

I need to travel to participate in training. Do you help with visa?
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We do not participate in the process of application for visa. However, you can use a confirmation that you have registered for our class and bought the ticket for it. This should support your visa application. If your application is declined, we will refund the ticket.

Why should I trust Altoros Training?
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Being a Google Gold Partner and AI Builder Partner Altoros delivers AI projects to the organizations that operate in various markets, like banking and finance, insurance, manufacturing, and others. An experienced AI team already helped 14 companies with machine learning adoption and delivering competitive advantage by utilizing the benefits of the technology. More than 500 people all over the world were trained and adopted the knowledge of our experts.