[코세나, kosena] Auto ML, H2O.ai의 보험분야 활용사례

H2O.ai
Insurance
Use Case Catalog
https://www.ﬂipsnack.com/B889C8BBDC9/h2o-ai-insurance-use-case-catalog-_-insurance-webinar-april-20
22/full-view.html

H2O.ai Conﬁdential
Democratizing
AI for
20,000
Organizations
6
OF THE TOP
INSURANCE
Companies
Partner with H2O.ai

v
Infrastructure
and Operations
Pricing &
Underwriting
Customer 360 Customer Behavioral Analysis
Cyber-threat Detection
Employee Retention
Precision Pricing
Automated underwriting
Reserve Predictions
Claims
Management
Call Centers/case routing
Claims processing
Damage Detection
Estimation
H2O.ai Use Case Catalog for Insurance
Meeting the needs of Insurance Customers
Fraud Claims Fraud
Customer
Experience
KYC Automation
Targeting
Propensity to Buy
Churn prediction
Click on each for in depth coverage across Insurance use cases.

v
Cyber-threat detection
Objective
● Help information security teams reduce risk and improve their
security posture efficiently and effectively
● Automate threat detection across large numbers of devices,
attack vectors, and data silos
Outcome
● Continuous assessment of asset inventory to gain a complete and
accurate view of devices, users and applications with access to IT
systems
● Models that can detect and respond to deviations from the norm,
even with noisy data
● Prediction models that will assess where and how a company is
most likely to be breached, so planning and resource allocation
can be directed toward weak points in the IT system
● Explainability of model recommendations and analysis for
security operations leaders, CISOS, auditors and Board of
Directors
Business Value
● Get up to date knowledge of global and industry level threats to
help prioritize defense systems
● Prevent cyber threat incidents and respond quicker/better when
they do happen, improving OPEX
● Free up limited cybersecurity teams to focus on complicated
cases, while AI takes care of routine tasks
H2O.ai’s AI and Data Approaches
● Classification Models that can identify threatening vs non
threatening events and actors
● Anomaly detection, entry classification, domain generation
detection
● Unsupervised learning for unlabeled data, clustering data based
on anomalies
● Analyze large data sets of events to identify many different types
of threats (eg, malware, ransomware, email phishing, malicious
code downloads)
● Train neural networks to tell the difference between malicious
and safe files
● Use images to train classifier neural networks to detect malware
in .doc and .pdf files
● Bias reduction models

v
Employee Retention
Objective
● Predict the risk of employee departure and identify relevant factors
● View predictions of employee departure, forecast churn rates and
identify relevant factors contained in employee data
Outcome
● Models with high accuracy of predicting employee departure and
identifying reasons for departure
Business Value
● Retain ﬂight-risk talent and thereby improve per employee
efficiencies
● Minimize cost of employee replacement due to backﬁlls
H2O’s AI and Data Approaches
● Automatic feature engineering, model validation, model tuning,
selection and deployment, machine learning interpretation (MLI),
recipes, time series and auto pipeline generation for model scoring
H2O AI App: Employee Churn Prediction

v
Precision Pricing
Objective
● Increase efficiency and accuracy of the entire pricing process
● React faster to rate changes with less actuarial and IT effort
● Improve model explainability and transparency for all lines of
business
Outcome
● Virtually eliminate model bias with automatic disparate impact
analysis for any model generated.
● Leverage new features gleaned from unstructured data through
OCR and NLP
● Reduce IT resources required to deploy and monitor models with
automated drift detection
● Automate the model documentation and filing process for state
regulators
Business Value
● Loss ratio improvement through enhanced segmentation and
targeted pricing
● Assure regulators and public that with unsurpassed model
explainability and interpretability
● Reduce risk and write more profitable, high quality business
H2O.ai AI and Data Approaches
● Automate the preparation of data pulled from from multiple
sources (eg, historical claims, policy and customer conversion and
retention data), with custom recipes to build models with the
insurance industry’s most widely used modeling platform
● Leverage automated feature engineering along with shapley
values for enhanced model interpretability and accuracy
● Provide product managers with “what if” pricing simulation AI
apps to predict impact of new pricing scenarios on profitability
and retention

v
Automated Underwriting
Objective
● Increase efficiency and accuracy of risk pricing process associated
with traditional manual underwriting processes, increasing
competitive advantage for insurers
● Widen the scope of data sources underwriters can use for
evaluation
Outcome
● Automate underwriting process including data collection, data
extraction, and filling forms
● Extract information from unstructured data through OCR and NLP
● Make models that can learn from 3rd party data sources, claims
histories and other past data to predict risk profile of new
submissions
● Deeper visibility into customer risk profiles
Business Value
● Tailor premiums to match each individual’s actual risk and optimize
pricing
● Shorten underwriting workflows creating near real-time service for
customer experience
● Improve underwriting profitability, while reducing OPEX, customer
churn, and costs for customer retention
H2O .ai AI and Data Approaches
● Use H2O recipes to automate the leveraging of data from
multiple sources (historical policy data, policyholder churn &
claims data to build models with LightGBM, Random Forest,
XGBoost and neural networks that outperform traditional GLM
approaches
● Train models with explainability and interpretability; understand
variable importance
● Use deep learning to process images and raw machine data

v
Reserve Prediction
Objective
● Use AI/Matching Learning to predict claim reserve amounts
based on historical based trends
● Accurately estimate the Reserve, and recalibrating these
estimates automatically when new information is added to the
claim record helps carriers react swiftly to ever changing events
Outcome
● Models that produce claims predictions that are aligned with actual
claims on an individual claim level, both in expected value and
variance.
Business Value
● Ensure that the size of reservoir of money Insurance Carriers hold is
adequately estimated the cost of future claims accurately, but not
excessive, to cover potential liabilities already assumed
● Improved transparency on future liabilities in order to ensure proper
statement of future liabilities on the balance sheet
H2O .ai AI and Data Approaches
● Machine learning techniques on a single, end-to-end platform
that leverages decision tree, SVM, neural network based and
deep learning based techniques
● Joint modeling of several numerical values (eg, paid losses and
claims outstanding)
● Make use of various claims data types (eg, numerical, categorical,
textual data) that can enhance prediction model understanding
and performance

v
Customer Handling and Call Center Optimization
Objective
● Improve customer experience and reduce handling costs
● Map the query by the customer to appropriate resolution section
Outcome
● An improvised automated method of Exploratory Data Analysis that
segregates the data and provides numerical & graphical analysis
based on the factors such as Date/Time, Location, Self Made
Complaints, user profiles and past interactions.
● Improvised NLP techniques and real time processing can accurately
pinpoint/classify the complaints into groups.
● Send high risk, complex complaints to the more experienced
customer support agents.
● A multi-classification AI model to give suggestions for possible
resolution.
● The model can also be used as a backend mapping engine for
Chatbots and Customer Issue Resolution applications.
● The solution can be delivered through APIs and custom application
Business Value
● Address the complaints of customers, maintain trust and ultimately -
retain.
● Improve case deflection, the rate that customers are able to find
their own answers to issues that they would have otherwise called
support for.
● Conduct Exploratory Data Analysis on Customer Complaint Data.
This data in the form of a Dataset will be Analysed, and numerical as
well as graphical analysis will be provided. Answers to questions
such as which location has the most complaints, what time period, is
the complaint made on behalf of the user or made by the customer,
etc. shall be provided in an organized manner.
● NLP Techniques will be used to process the data, which can be in
the form of a dataset or realtime.
● H2O AI App, built with H2O Wave, an SDK for Data Scientists:
○ Import Data and Data Prep ( Complaint Data)
○ Exploratory Data Analysis to analyse the aforementioned points.
○ Interactive Dashboard where insights are displayed based on the
searched tags, percentage rank of complaints ( #1 Rude
behaviour, #2 Issues, etc.) There will also be percentage
comparisons as the data increases ( Complaints in xxx domain
are 15% higher, etc.) Top increases, top decreases
○ Natural Language Processing Apps
● Applying NLP models to obtain tags based on meanings.
○ Text Sentiment Apps

v
Claims Processing
Objective
● Infuse the claims processing workflow using AI to inform adjusters with
smart actions
● Quickly and accurately estimate and process claims
● Use AI to automate repetitive claims processes and streamline claims
processing, allowing those with low risk to be processed automatically
while higher risk claims are routed to investigators for review
● Predict claims payments at individual level
Outcome
segregates the data and provides numerical & graphical analysis based
on factors such as Date/Time, Location, Self Made Complaints, user
profiles and past interactions.
segregates the data and provides numerical & graphical analysis based
on factors such as Date/Time, Location, Self Made Complaints, user
profiles and past interactions.
● NLP techniques and real time processing can accurately
pinpoint/classify the complaints into groups.
● Send high risk, complex complaints to the more experienced customer
support agents.
● A multi-classification AI model to give suggestions for possible
resolution.
● The model can also be used as a backend mapping engine for Chatbots
and Customer Issue Resolution applications.
● The solution can be delivered through APIs and custom application
● Build multiple models that can analyze all incoming structured and
unstructured data, scanned images, faxes and natural language sets,
policy information, medical bills reports, and legal documents to offer
real time recommendations to claims adjusters.
● Each time new claims data is added to a record, apply algorithms to
accurately score for fraud, reserve changes, and claim complexity at a
level of nuance that is often missed through manual reviews.
● Build models that can route high complexity claims to seasoned
adjuters for review, generating smart alerts with reason codes to take
immediate action; Low complexity, low fraud claims are automatically
flagged for straight through processing
Business Value
● Fewer touches
● Claims adjusters react fast to critical events
● Faster claims settlement
● Reduced ALAE

v
Damage Detection and Estimation
Objective
● Increase granularity and accuracy for damage estimation
● Accurately detect and classify damages from images
Outcome
● Build models that can detect and classify damages from images,
isolating where in the image there is damage and what type of
damage is represented
● Translate damage into individual parts affected by adding additional
information to the analysis, such as parts listed needed to repair,
model optimum reconditioning scenarios
● Retrieve images of similar items (eg, vehicles), both damaged and
undamaged, to help estimators evaluate whether or not the item
was actually damaged
Business Value
● Shorten processing time for property and casualty damage claims,
improving customer satisfaction with faster, more accurate
settlements
● Equip claims estimators with signals to identify where to focus
attention during damage assessment, and reasons for it
● Route damage estimates to the right estimator team
● H2O’s image recognition and deep learning capabilities that can
predict vehicle damage severity using multiclass classification
● Use binary classsification for vehicle damage identification
● Use multiclass classification of vehicle make, model, and year
● Use H2O Wave to make AI applications that allow business
stakeholders to get:
○ a pre-selection of cases where pictures do not match with
expectations
○ highlighted images where cases do not match claims reports
○ recommendations for next steps
● Explainable AI visualizations and narratives that explain why the
model made its damage estimation

Deliver on Customer Experience

v
Targeting
Objective
● Send messages/content at the right time with the right content
based on individual and collective customer behaviors
● Help marketing teams at FIs achieve more awareness, higher
conversions and better engagements
Outcome
● Pool large data sets to accurately predict custom audience
segments with specific attributes relevant to the brand
● Identify unique and relevant audiences for the brand that will be
in line with the audience data pools initially created
Business Value
● Increase click through rates by 4x (industry expectations)
● Reduce costs per engagement
● Machine learning interpretability (MLI) that provides targeting
explanations
● Ability to provide computational power to advance granular
audience targeting capabilities
● Target identification models - automate and optimize processes
for potential consumer identification, information extraction and
market segmentation
● Probability models that will predict level at which consumers will
click on ads, click through rates, etc
● Recommendation models - discover useful patterns to determine
what the user finds interesting or not
● Machine learning interpretability that provides targeting
explanations
H2O AI Apps, built with H2O Wave, an SDK for Data Scientists:
● UMAP-based clustering App
● Customer Journey / Lead Scoring App

v
Objective
● Expand offers to customers with accuracy and speed by
implementing predictive analytics
● Forecast behaviors of a target audience by analyzing their past
behaviors
Outcome
● Cut time to build models from 6 months to less than a week
● Doubled customer propensity to buy rate
● Realized additional revenue by being better able to target offers
● Created propensity models that helped companies identify the
right customers and prospects that have a high likelihood to
purchase a particular product or service
Business Value
● Optimize promotions based on knowing which leads to sell to
with the right message and product/services
● Create models and use custom recipes that generate
features/variables that provide a probabilistic estimation of
whether customers will perform any of such actions or not i.e. a
propensity score.
● Use propensity scores to estimate value each customer brings in
real-time. Make data available to Relationship Managers of the
Bank (RMOs) who can further slice and dice the data and
consume the information intuitively.
● H2O AI Apps, built with SDK for Data Scientists, can perform:
○ Data Preparation
○ Supervised Machine Learning
● Automatic feature engineering, model tuning and
optimization, scoring pipeline generation
● Accurate time series capabilities
● Automatically generated visualizations and data plots
○ Nonlinear algorithmic modeling
○ Binary classiﬁcation
○ Results viewed on Dashboards in a tabular format, grouped by
different features (city, areacode, spending limit, etc) or for a
single user; view target groups and selected features such as
textual insights explaining the prediction, the propensity
score for each user (mean or median) if a target group is
selected.
Propensity to Buy

v
Customer Churn
Objective
● Predict customer churn based on behavioral patterns and
cadence of transactions
● Identify leading indicators for churn, improving customer
retention insights
Outcome
● Optimized models up to the granularity of each customer
● Calculated daily probability for customer churn for early
detection
● Distributed in-memory infrastructure trains and scores entire
customer base in minutes instead of hours
● Reduced model building time from 6-7 hours to < 30 min
Business Value
● Intercepted customer churn before it happened
● Know what leads a client towards the decision to leave the
company.
● Develop loyalty programs and retention campaigns to keep as
many customers as possible.
● Improving the customer retention rate for existing customers
by just 5 percent can improve a company’s proﬁtability by 25 to
95 percent (Bain & Co).
● Identify customers with high probability of churning by creating models and
custom recipes speciﬁcally built for generating features/variables.
● Distributed algorithms (Random Forest, GBM)
● Capabilities to iterate through models with different parameters within time
constraints, instead of relying on just one model
● H2O Customer Churn AI App, built with H2O Wave:

v
Claims Fraud
Objective
● Find new ways to improve fraud detection accuracy and
detection time across claims fraud types
● Use AI to generate new fraud rules
● Detect anomalous patterns for individuals, accounts and
networks within and across portfolios
● Target different groups and subsets in a tailored approach,
comparing different inferences from these subsets with the
same inferences from the entire population
● Flag specific repetitive fraud groups and deploy counter
measures
Outcome
● 6x faster development of state-of-the-art ML models that
pre-empted fraudulent claims
● Build AI models and use custom recipes specifically built for generating
features/variables that provide associated information about fraudulent
behavior. This data is then available to the fraud investigator who can further
slice and dice the data and consume the information intuitively.
● Feature engineering with Deep Learning to model new and complex attack
patterns quickly
● Behavior profiling for data networks - IP addresses, buying patterns
● Terabytes of data leveraged to deliver high scalability and performance, flexible
deployment and integration with other big data frameworks
● Model Explainability aids investigators in understanding the why the model
thinks there is potential fraud.
Business Value
● Reduce fraud losses and improve customer
experience
● 2x increase in accurate claims fraud detection
● 11% improvement in accuracy resulting in $1M
saved monthly per basis point

v
Know your customer (KYC) Automation
Objective
● Operationalize customer data to understand behavior analytics for several use
cases across pricing, claims management, fraud prevention and more
● Identity verification
Outcome
● Create a foundation for customer analytics
● sA series of models that can assess customer analytics, behaviors and needs
across very different customer profiles
Business Value
● Improve campaign effectiveness
● Accurate, targeted cross-sell / up-sell
● Retain your most profitable customers
● Deliver real-time superior customer experience at all
points of service
● Increase customer lifetime value
● Verify that your customers are who they say they are
and assessing the risks associated with each customer
like the possibilities of fraud, money laundering,
terrorism financing, etc.
● Improve digital onboarding experience with automated
KYC processes, reducing application abandonment.
● Process large quantities of customer data and integrate
into the H2O AI Cloud to build KYC models
● Probabilistic and Fuzzy Matching
● Document verification, NLP and image recognition to
verify and authenticate customer information
● Seamlessly extract customer data and integrate with
existing customer management or onboarding systems
Customer Data Integration
Social Profile Credit Profile Life Insurance
Profile
Mortgage Account
Profile
Bank Account
Profile
Customer
360º
View
Master Data Management
H2O AI Engine /
Probabilistic Matching

v
Customer Behavior Analysis
Objective
● Segment users on the basis of different attributes such as -
demographics data, transaction history, transaction behavior
(Recency, Frequency, Monetary).
● Rapidly identify and evaluate customer behavior signals to take
action on potential fraudulent activity, optimize marketing
activities, etc
Outcomes
● Create behavior scoring models
● Continuously update customer data
Business Value
● Create foundation for:
○ Personalization strategies to improve customer experience,
customer sentiment
○ Targeting different groups in a tailored approach
○ Recommending core banking products for cross-selling and
up-selling, increase sales, revenues, and profits
○ Deploying customer churn prevention schemes
H2O’s AI, Data-Driven Approach:
● Provide an end-to-end pipeline that collects and updates the
customer data, performs data preparation, performs
unsupervised machine learning, and generates clustering results.
● Use H2O Wave (SDK for Data Scientists) to build an AI App that
can perform:
○ Data Preparation
○ Unsupervised Machine Learning
○ Rule fit analysis to identify cluster rules
○ Additional analysis such as association rules mining to identify
prominent rule patterns
○ Insights via a Clustering Dashboard
○ Tailored schemes of users
○ Graph Neural Network
○ Business metrics as defined by the end user need
○ Trigger retraining options

sales@h2o.ai
Contact

[코세나, kosena] Auto ML, H2O.ai의 보험분야 활용사례

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