Key Insights from InsurTech Bootcamp Day 2

Location:
Boston Fintech Week 2019
Babson College
Boston, MA
Fintech Bootcamp
Day 2
2019 Copyright QuantUniversity LLC.
Presented By:
Sri Krishnamurthy, CFA, CAP
sri@quantuniversity.com
www.analyticscertificate.com

2
QuantUniversity
• Analytics and Fintech Advisory
• Trained more than 1000 students in
Quantitative methods, Data Science
and Big Data & Fintech
• Programs
▫ Analytics Certificate Program
▫ Fintech Certification program
• Building

• Founder of QuantUniversity LLC. and
www.analyticscertificate.com
• Advisory and Consultancy specializing in Data
Science, ML and Analytics
• Prior Experience at MathWorks, Citigroup and
Endeca and 25+ financial services and energy
customers.
• Charted Financial Analyst and Certified
Analytics Professional
• Teaches Analytics in the Babson College and at
Northeastern University, Boston
Sri Krishnamurthy
Founder and CEO
3

7
Process Optimization &
Automation
Channel partnerships
New products due to
demands
Geographic expansion &
customization to local needs
Connected devices: Increased
use of analytics, behavior
tracking & use of Telematics
Mobile, Online digital
channels
On-demand & new products
for novel use cases
Beyond typical PC/Life
Insurance products
Corporate
Consumer
Key innovations

8
Case 1 – Roof type classification

9
Case 1 – Roof type classification

40 WALL STREET, NEW YORK, NY www.rblt.com
KEY INSURTECH INSIGHTS
Sep 2019
Dushyant Shahrawat
Director, FinTech Investment Banking

www.rblt.comwww.rblt.com
What Is It?
12
PERSONAL LINES COMMERCIAL LINES
Auto Home
Property &
Casualty
Workers
Compensation
LifeHealth Group Health
and Life
Cyber
AND THEN THERE IS…
Wedding cold feet, Alien abduction, Body part, Chicken insurance

How Large Is It?
13Source – InsureTech Connect, Oliver Wyman
Insurance Sector Size By Premiums Written

How Is The Process Changing?
14
Quote
Issue Bind
Pricing
Underwriting
Claims &
Settlement
Policy
Admin &
Central
Systems
• New Sales Models
• Shopping Sites
• Telematics, IoT
• Alternative Data
• Chatbots
• New Processing Systems
• Drones
• Digital Disbursements

What Are The New InsurTechs Doing?
15

Key Insights
16
InsurTech is in a multi-year growth phase attracting
record amounts of capital since 2014
§ Unlike other FinTech verticals that show signs of
slowdown, Series A deal activity is still robust in
InsurTech
§ At a projected $5B for full year 2019, financing
could set a record this year
§ Funding has grown at 60% CAGR over 2014-
1H’2018
Strategic investors are playing a much larger role in
InsurTech financing than in other FinTech verticals
§ Strategic investors participated in over 50% of
InsurTech financings in Q2 2019 (versus 35% in
other verticals)
§ SoftBank was the biggest investor in Q2 2019:
Lemonade ($300M), Collective Health ($205M),
Policy Bazaar ($152M)
Good news for InsurTech CEOs. The size of funding
rounds and valuations are both rising
§ Median equity capital raised is growing over
funding rounds: median Series A is $11M while
Series C is $51M
§ Median valuations for Series A have gone up the
most: 2.5x from $17M (2014) to $43M (2019).
Series B valuations have also risen in line with
Series A while Series C is also increased, although
at a more measured pace than Series A/B.
Like in other FinTech verticals, B2C InsurTechs (except
Life insurance) attract more funding than B2B firms
§ Two consumer-facing sectors, Health (53%) and
Personal Lines (26%) have attracted 80% of funding
§ Life, Commercial and Multiline are still in early
stages, attracting between 5-8% of total capital
§ While B2C InsurTechs (Health, Personal Lines)
attract more funding, B2B InsurTechs (P&C) attract
greater interest from acquirers
M&A acquirers are growing in number and variety
including PEs, Strategics, Tech firms and even Telcos
§ InsurTech CEOs have a growing list of potential
acquirers to sell their firms to (310 and growing)
§ Carriers have become active buyers of InsurTechs,
especially Allianz, AXA and MunichRe
Median pre-money valuations vary widely by subsector
indicating big differences in investor appetite
§ Personal Lines (Auto, Home) are valued at $55M,
followed by Health, which is valued at $37M
§ Commercial, Multiline and Life valuations fall within a
narrow range ($26M, $24M, $21M respectively)
1
5
6
2
4
3

$737
$2,790 $2,460
$4,340
$4,730
$2,510
$0
$1,000
$2,000
$3,000
$4,000
$5,000
$6,000
2014 2015 2016 2017 2018 2019e
InsurTech Continues To Attract Significant Funding
17
C a p i ta l R a i s e ($ Millions)
CAGR: 60%
Largest
Capital Raise
Company
Select
Investor
88
119
$80M
Series C
$720M Pre-
Money
154
200
$934M
PE Growth
~$7B Pre-Money
Undisclosed
196
274
$500M
Series A
~$2.5B Pre-Money
252
389
$160M
Series B
$240M Pre-
Money
265
393
$950M
PE Growth
~$2.2B Pre-Money
131
165
$500M
Series E
> $1B Pre-Money
Deals
(w/ Reported $ Value)
Total Deals
2H’2019 (Annualized)1H’2019 (Actual)
Source – RBLT analysis, Pitchbook, FactSet, CrunchBase, CB Insights, other industry sources
# of Companies
(CY’14 – Q2’2019)
738
# of Deals
(CY’14 – Q2’2019)
1,086
Total Deal Value
(CY’14 – Q2’2019)
$17.6B
Unicorns Created
(w/ Reported Valuation)
(CY’14 – Q2’2019)
16

Funding League Table Highlights Sector Vibrancy
18
$3,818
$1,755
$1,279
$1,034
$950$925
$598
$521$480$464$442$369$355$285$220$216$210$178$164$154
To p 2 0 I n s u r Te c h s by To ta l C a p i ta l R a i s e ($ Millions | As of Q2’2019)
LH&A
LH&A
P&C LH&A LH&A
LH&A ML
P&C
MLLH&A
P&C
P&C ML LH&A P&C
LH&A LH&A ML
LH&A
LH&A
Key: LH&A (Life, Health, and Annuity) | P&C (Retail and Commercial Lines) | ML (Multiline Insurers, Line of Business Agnostic)

$55M, $10M, $1,229M
$26M, $5M, $411M
$24M, $10M, $232M
$37M, $10M, $2,538M
$21M, $9M, $335M
$4M
$5M
$6M
$7M
$8M
$9M
$10M
$11M
$12M
$10M $15M $20M $25M $30M $35M $40M $45M $50M $55M $60M
MedianCapitalRaise
Median Valuation (Pre-Money)
B2C Models Attract More Investor Interest Than B2B
19
35
44
P&C (Personal Lines)
35
41
P&C (Commercial Lines)
9
13
Life & Annuity
34
41
Health
Company Count
Deal Count
20
25
Multiline
R e l a t i v e Va l u a t i o n a n d C a p i t a l I n f l o w B y S u b s e c t o r
(United States | CY’18 – Q2’19 | VC & Growth Equity)
KEY
• Median Valuation, Median Capital Raise, Total Capital Invested
• Relative size of bubble represents “Total Capital Invested”
Note: Life & Annuity (Health IQ’s Series D, E valuation figures excluded from analysis)
Life & Annuity
Multiline
Health
P&C (Personal Lines)
P&C (Commercial Lines)

45 46 50
107
119
32
0
20
40
60
80
100
120
140
CY'2014 CY'2015 CY'2016 CY'2017 CY'2018 CY'2019e
M&A Is Led By Strategic Investors
20
M & A A c t i v i t y (PE, Strategic M&A)
Select
Large Deal
Target
Acquirer
51%
$1,800B
↑ 2 %
72%
$760M
↑ 9 %
56%
$7,500M
↑ 114 %
64%
$3,000M
↑ 11 %
71%
$1,578M
↓ 46 %
56%
$1,400M
CAGR: 28%
% ∆ YoY
% Strategic
M&A
# of Deals
(CY’14 – Q2’2019)
400
Total Deal Value
(CY’14 – Q2’2019)
$51.7B
2H’2019 (Annualized)1H’2019 (Actual)

www.rblt.comwww.rblt.com 21
Please Contact Us To Discuss More:
Vikas Shah
I n v e s t m e n t B a n k i n g
2 1 2 - 6 0 7 - 3 1 0 0
v s h a h @ r b l t . c o m
Dushyant “D” Shahrawat
I n v e s t m e n t B a n k i n g
2 1 2 - 6 0 7 - 3 1 8 0
d s h a h r a w a t @ r b l t . c o m
Copyright 2019. Rosenblatt Securities Inc. All rights reserved.
Rosenblatt Securities Inc. seeks to provide and receive remuneration for Agency Brokerage, Market Structure Analysis, Macro and other
Sector Analysis and Investment Banking Advisory Services. Rosenblatt Securities Inc. may, from time to time, provide these services to
companies mentioned in this analysis. This material is not a research report and should not be construed as such, and does not contain
enough information to support an investment decision. Neither the information contained herein, nor any opinion expressed herein,
constitutes the recommendation or solicitation of the purchase or sale of any securities or commodities. The information herein was
obtained from sources which Rosenblatt Securities Inc. believes reliable, but we do not guarantee its accuracy. No part of this material may
be duplicated in any form by any means. Member NYSE, FINRA, SIPC.

AI and Machine Learning in Finance

23
The 4th Industrial revolution is Here!
Source: Christoph Roser at AllAboutLean.com
As per Wikipedia*, “The 4th Industrial Revolution ….. marked by emerging technology breakthroughs in a
number of fields, including robotics, artificial intelligence, nanotechnology, quantum computing, biotechnology,
the Internet of Things, the Industrial Internet of Things (IIoT), decentralized consensus, fifth-generation wireless
technologies (5G), additive manufacturing/3D printing and fully autonomous vehicles.”
* https://en.wikipedia.org/wiki/Fourth_Industrial_Revolution

24
Scientists are disrupting the way we live!
Source: https://www.ladn.eu/tech-a-suivre/mobilite-2030-vehicules-volants-open-data/

25
Interest in Machine learning continues to grow
https://www.wipo.int/edocs/pubdocs/en/wipo_pub_1055.pdf

26
MACHINE LEARNING AND AI IS REVOLUTIONIZING FINANCE

27
Market impact at the speed of light!
27

28
Machine Learning & AI in finance: A paradigm shift
28
Stochastic
Models
Factor Models
Optimization
Risk Factors
P/Q Quants
Derivative pricing
Trading Strategies
Simulations
Distribution
fitting
Quant
Real-time analytics
Predictive analytics
Machine Learning
RPA
NLP
Deep Learning
Computer Vision
Graph Analytics
Chatbots
Sentiment Analysis
Alternative Data
Data Scientist

29
CFA Institute has adopted Fintech and AI content in its curriculum
Ref: https://www.cfainstitute.org/-/media/documents/support/programs/cfa/cfa-program-level-iii-fintech-in-investment-management.ashx

30
The Virtuous Circle of
Machine Learning and AI
30
Smart
Algorithms
Hardware
Data

31
The rise of Big Data and Data Science
31
Image Source: http://www.ibmbigdatahub.com/sites/default/files/infographic_file/4-Vs-of-big-data.jpg

32
Smart Algorithms
32
Distributing Computing Frameworks Deep Learning Frameworks
1. Our labeled datasets were thousands of times too
small.
2. Our computers were millions of times too slow.
3. We initialized the weights in a stupid way.
4. We used the wrong type of non-linearity.
- Geoff Hinton
“Capital One was able to determine fraudulent credit
card applications in 100 milliseconds”*
* http://go.databricks.com/hubfs/pdfs/Databricks-for-FinTech-170306.pdf

33
Hardware
Speed up calculations with
1000s of processors
Scale computations with
infinite compute power

35
• Machine learning is the scientific study of algorithms and statistical
models that computer systems use to effectively perform a specific task
without using explicit instructions, relying on patterns and inference
instead1
• Artificial intelligence is intelligence demonstrated by machines, in
contrast to the natural intelligence displayed by humans and animals1
Definitions: Machine Learning and AI
35
1. https://en.wikipedia.org/wiki/Machine_learning
2. Figure Source: http://www.fsb.org/wp-content/uploads/P011117.pdf

Machine Learning Workflow
Data Scraping/
Ingestion
Data
Exploration
Data Cleansing
and Processing
Feature
Engineering
Model
Evaluation
& Tuning
Model
Selection
Model
Deployment/
Inference
Supervised
Unsupervised
Modeling
Data Engineer, Dev Ops Engineer
Data Scientist/QuantsSoftware/Web Engineer
• AutoML
• Model Validation
• Interpretability
Robotic Process Automation (RPA) (Microservices, Pipelines )
• SW: Web/ Rest API
• HW: GPU, Cloud
• Monitoring
• Regression
• KNN
• Decision Trees
• Naive Bayes
• Neural Networks
• Ensembles
• Clustering
• PCA
• Autoencoder
• RMS
• MAPS
• MAE
• Confusion Matrix
• Precision/Recall
• ROC
• Hyper-parameter
tuning
• Parameter Grids
Risk Management/ Compliance(All stages)
Analysts&
DecisionMakers

37
1. Data
2. Goals
3. Machine learning algorithms
4. Process
5. Performance evaluation
Key steps involved

39
Dataset, variable and Observations
Dataset: A rectangular array with Rows as observations and
columns as variables
Variable: A characteristic of members of a population ( Age, State
etc.)
Observation: List of Variable values for a member of the
population

40
Variables
A variable could be:
▫ Categorical
– Yes/No flags
– AAA,BB ratings for bonds
▫ Numerical
– 35 mpg
– $170K salary

41
Longitudinal
▫ Observations are dependent
▫ Temporal-continuity is required
Cross-sectional
▫ Observations are independent
Datasets

42
Data
Cross
sectional
Numerical Categorical
Longitudinal
Numerical
Summary
42

44
• Descriptive Statistics
▫ Goal is to describe the data at hand
▫ Backward-looking
▫ Statistical techniques employed here
• Predictive Analytics
▫ Goal is to use historical data to build a model for prediction
▫ Forward-looking
▫ Machine learning & AI techniques employed here
Goal
44

45
• Given a dataset, build a model that captures the
similarities in different observations and assigns
them to different buckets- Clustering
• Given a set of variables, predict the value of
another variable in a given data set- Prediction
▫ Predict salaries given work experience, education etc.
▫ Predict whether a loan would be approved given fico
score, current loans, employment status etc.
Predictive Analytics : Cross sectional datasets
45

46
Goal
Descriptive
Statistics
Cross
sectional
Numerical vs
Categorical
Categorical vs
Categorical
Numerical vs
Numerical
Time series
Predictive
Analytics
Cross-
sectional
Segmentation Prediction
Predict a
number
Predict a
category
Time-series
Summary
46

48
Machine Learning
Unsupervised Supervised
Reinforcement Semi-Supervised
Machine Learning

49
Goal
Descriptive
Statistics
Cross
sectional
Numerical vs
Categorical
Categorical vs
Categorical
Numerical vs
Numerical
Time series
Predictive
Analytics
Cross-
sectional
Segmentation Prediction
Predict a
number
Predict a
category
Time-series
Machine Learning Algorithms
49

50
Supervised Algorithms
▫ Given a set of variables 𝑥", predict the value of another variable 𝑦 in
a given data set such that
▫ If y is numeric => Prediction
▫ If y is categorical => Classification
▫ Example: Given that a customer’s Debt-to-Income ratio increased 20%, what are
the chances he/she would default in 3 months?
Machine Learning
50
x1,x2,x3… Model F(X) y

51
Unsupervised Algorithms
▫ Given a dataset with variables 𝑥", build a model that captures the
similarities in different observations and assigns them to different
buckets => Clustering
▫ Example: Given a list of emerging market stocks, can we segment them
into three buckets?
Machine Learning
51
Obs1,
Obs2,Obs3
etc.
Model
Obs1- Class 1
Obs2- Class 2
Obs3- Class 1

52
• Parametric models
▫ Assume some functional form
▫ Fit coefficients
• Examples : Linear Regression, Neural Networks
Supervised Learning models - Prediction
52
𝑌 = 𝛽' + 𝛽) 𝑋)
Linear Regression Model Neural network Model

53
• Non-Parametric models
▫ No functional form assumed
• Examples : K-nearest neighbors, Decision Trees
Supervised Learning models
53
K-nearest neighbor Model Decision tree Model

54
Machine
Learning
Supervised
Prediction
Parametric
Linear
Regression
Neural
Networks
Non-
parametric
KNN
Decision
Trees
Classification
Parametric
Logistic
Regression
Neural
Networks
Non
Parametric
Decision
Trees KNN
Unsupervised
algorithms
K-means
Associative
rule mining
Machine Learning Algorithms
54

55
Machine Learning movers and shakers
Deep
Learning
Automatic
Machine
Learning
Ensemble
Learning
Natural
Language
Processing

56
http://www.asimovinstitute.org/neural-network-zoo/

58
The Process
58
Data
ingestion
Data
cleansing
Feature
engineering
Training
and testing
Model
building
Model
selection

59
• What transformations do I need for the x and y variables ?
• Which are the best features to use?
▫ Dimension Reduction – PCA
▫ Best subset selection
– Forward selection
– Backward elimination
– Stepwise regression
Feature Engineering
59

60
Data
Training
80%
Testing
20%
Training the model
60

62
Evaluating
Machine learning
algorithms
Supervised -
Prediction
R-square RMS MAE MAPE
Supervised-
Classification
Confusion Matrix ROC Curves
Evaluation framework
62

63
• Fit measures in classical regression modeling:
• Adjusted 𝑅, has been adjusted for the number of predictors. It increases
only when the improve of model is more than one would expect to see by
chance (p is the total number of explanatory variables)
𝐴𝑑𝑗𝑢𝑠𝑡𝑒𝑑 𝑅, = 1 −
⁄∑"8)
9
(𝑦" − ;𝑦"), (𝑛 − 𝑝 − 1)
∑"8)
9
𝑦" − ?𝑦"
, /(𝑛 − 1)
• MAE or MAD (mean absolute error/deviation) gives the magnitude of the
average absolute error
𝑀𝐴𝐸 =
∑"8)
9
𝑒"
𝑛
Prediction Accuracy Measures

64
▫ MAPE (mean absolute percentage error) gives a percentage score of
how predictions deviate on average
𝑀𝐴𝑃𝐸 =
∑"8)
9
𝑒"/𝑦"
𝑛
×100%
• RMSE (root-mean-squared error) is computed on the training and
validation data
𝑅𝑀𝑆𝐸 = 1/𝑛 H
"8)
9
𝑒"
,
Prediction Accuracy Measures

65
1. Data
2. Goals
3. Machine learning algorithms
4. Process
5. Performance Evaluation
Recap

#Disrupt19
Sentiment Analysis Using Natural Language Processing in Finance

• What is Sentiment Analysis?
• The Case study Setup
• Design Choices
• The Pipeline
• Demo
#Disrupt19
Agenda

69
What is NLP ?
AI
Linguistics
Computer
Science

70
• Q/A
• Dialog systems - Chatbots
• Topic summarization
• Sentiment analysis
• Classification
• Keyword extraction - Search
• Information extraction – Prices, Dates, People etc.
• Tone Analysis
• Machine Translation
• Document comparison – Similar/Dissimilar
Sample applications

72
• The process of computationally identifying and categorizing
opinions expressed in a piece of text, especially in order to
determine whether the writer's attitude towards a particular
topic, product, etc. is positive, negative, or neutral.
Sentiment Analysis
#Disrupt19

73
• Understanding sentiments in Earnings call transcripts
Goal
73

74
• Interpreting emotions
• Labeling data
Options
• APIs
• Human Insight
• Expert Knowledge
• Build your own
Challenges

75
NLP pipeline
Data Ingestion
from Edgar
Pre-Processing
Invoking APIs to
label data
Compare APIs
Build a new
model for
sentiment
Analysis
Stage 1 Stage 2 Stage 3 Stage 4 Stage 5
• Amazon Comprehend API
• Google API
• Watson API
• Azure API

Thank you!
Sri Krishnamurthy, CFA, CAP
Founder and CEO
QuantUniversity LLC.
srikrishnamurthy
www.QuantUniversity.com
Contact
Information, data and drawings embodied in this presentation are strictly a property of QuantUniversity LLC. and shall not be
distributed or used in any other publication without the prior written consent of QuantUniversity LLC.
78

Key Insights from InsurTech Bootcamp Day 2

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