Allianz Partners - Insurer Innovation Award 2022

1. MACARENA
An AI voicebot for
a fully automated
home claims first
notice of loss &
steering
The Digital Insurer 2022 Awards
Insurer Innovation Award - EMEA July, 2022

2. Allianz Partners is leading the transformation of
Customer Experience in Home Assistance
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Partners
Best practices
transfer:
Active presence
in 21 countries
Data
availability:
Over 2 Million
claims & 6
Million calls
handled per
year
Deep Knowledge:
Over 70 years of
experience in
Assistance &
claims
management
Advanced digital
partner
For home
insurers,
providing direct
return on CX,
quality and
efficiency
Making digital
transformation
happen:
An innovation
Hub using NLP,
AI and ML in
disruptive
cases of use

3. Our vision: a comprehensive digital ecosystem for
Home claims management
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Partners
Aseamless omnichannel Customer Experience that continuously gathers information and leads to the most efficient decision
Claim
occurs
sMArt Contract
verifies the cover
of the claim and
assign causes/
guarantees /
deductibles /
depreciation/liabi-
lity determination
Contract
Analyses & provides
accurate estimate
based on MA RIK
historic data
• Predictive models for
cost price/quality
• MAWeather
• Machine learning
Predictive
Models
• Claim handler
• Agent
• Broker
• Self Service
Claim
notification
• Script
• Claim
notification
• NLP
Digital Dialog
MARules
provides
handler with
preferred
service
path/liability
determination
Settlement Process
Next Best Action
Rejection after
collection of
documents and
visit
Cash settlement
after collection of
document and visit
RIK
Rejection in
notification
Cash settlement
in notification
Video expertise
in notification
Surveyor visit
Trade
professional visit
Loss adjuster
visit
Automated notification and
Claims steering based on
Voicebot, Smart Contract and
intelligent claims steering
based on rules and algorithms

4. An ongoing evolution: from a
test & learn experience to a success story
4
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The 4th Generation ofAutomated FNOL was born based on Macarena
• Agent knowledge-based
• Guarantees and
calculations directly applied
by agent
• High formative costs
• Human biased process:
error 1: cover uncovered;
error 2: uncover covered
• Focused on average
rejection rate not single
case focus
• Predefined dialogue
between agent-policyholder
• Coverage only evaluated at
product level
• Tool proposes guarantees
to activate, agent takes final
decision
• Pareto rule: 70% of cases in
7 groups (each causes
group)
• Non time-cost efficient
• Quick cause & damages selection
• Claim coverage evaluated at
policy level: product + contracted
guarantees + settlement
calculations
• Close to insured semantic
declaration (damages)
• Time-cost efficient process
• Further details data gathering:
Having accurate info at damage
level allows better predictions,
improving operational process
(info could be leveraged in
underwriting)
• AI -NLP layer translates insured
declaration to smart contract
terms & conditions (Macarena)
• Aimed at a better customer
experience: early identification,
easy to declare, total transparency
• Building a digital experience from
claim opening + productivity
enhancer
• Working to pass it to other
channels: WhatsApp, google
assistant, Alexa, telegram…
Traditional process Decision tree guided
process (2017-18)
Claim classification / sMArt
contract (2018-19)
Fully automated FNOL and
Steering (2019-20)

5. NLP voicebot implementation
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Developing a home insurance natural language processing (NLP) engine from scratch
Corporate ops & senior
CC agents develop
theoretical NLP model
assigning frequent
expressions to sMArt
digital products causes
& damages
By analysing pre-
recorded real CC
conversations we identify
localisms, colloquial
expressions, and new
damages expressions
which should be added
to the model
Model 1: recorded
conversation testing
Model 2: NLP bar refining
CC agents during real claim
openings use NLP engine to
identify causes and damages
from policyholder’s direct
speech manual transcription
When NLP engine doesn’t
provide right matching, CC
agent recalibrate to correct
cause & damages output
Model 3: Real Voicebot
coupling
Extremely accurate machine
transcription is sent to NLP
engine to identify causes
(intents) and damages (entities)
in real policyholder’s speech
System starts with a few
causes configured as
automatic, progressively scope
is broadened to all possible
claims causes
Base Model
NLP proposal & CC agent agree: NLP
is trained reinforcing score of prediction
Matching between NLP
and CC agent:
NLP proposal & agent disagree: NLP is
trained assigning right cause &
damages output
Human auditing of cause &
damages accuracy
NLP proposal is right: NLP is trained
reinforcing score of prediction
NLP proposal is wrong: NLP is trained
assigning right cause & damages output
Audit process

6. 100% automation of first notice of loss & steering
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OUT: Automatic
(y/n) & guarantees
activated
OUT: Active
guarantees, product
2. ID validation
5. Intention
3. Name & surname
WS1A: Identification
4. Policy address
6. Claim date
8. Claim details
WS2: Coverage
10. Contact details (phone)
12. VAT script
WS3: Appointment
scheduling
14. Availability
1. Initial presentation
INCOMING CALL
7. Policy valid?
13. Repair?
WS4: New claim - manual
WS4: New claim - automatic
9. Automatic?
15. Appointment scheduling
11. Contact details 3rd party (phone).
WS1B: Guarantees
OUT: Policy no,
address, policy
holder
IN: Company, ID
no.
IN:Policy no, Claim
date
IN: Cause&damages,
product, active
guarantees
IN: Trade, starting
date
OUT: Free slots
6
Policy holder data and
information gathering to
assess the claim
based on NLP
sMArt Contract to
confirm coverage/non
coverage of the
damage and other
aplicable conditions
(depreciations,
excesses, etc.)
Automated
application of rules to
assign the most
suitable repairing
service based on the
damage, client and
location.

7. Developing a home claims insurance NLP structure
7
Exhaustive data base of causes, expressions, damages, exclusions, exceptions among others
Id Descripción
100 CRISTALES - Impacto
110 CRISTALES - Placas vitrocerámicas
120 CRISTALES - Otras causas
130 LOZA SANITARIA. FREGADERAS
140 MÁRMOLES, GRANITOS Y SIMILARES
200 DAÑOS AGUA - Rotura tubería empotrada
201 DAÑOS AGUA - Rotura tubería vista
202 DAÑOS AGUA - Obturación, atasco
210 DAÑOS AGUA - Avería de un tercero reparada
… …
Id Descripción
DA100 Tubería empotrada
DA101 Taladro en tubería
DA105 Sale agua por los azulejos o similar
DA106 Tubería vista
DA107 Grifos y llaves de paso
DA108 Atasco tuberías y desagües
DA111 Filtraciones por banda de sanitario
DA420 Pintura
DA480 Moqueta
DA500 Parquet
… …
Exclusions
Urgencies
Claim handler
70 causes → ~10.000
expressions
classified as intents
250 damages →
~20.000 expressions
classified as entities
100 exclusions and
exceptions →
~10.000 expressions
Extractfrom database

8. Complex integration for a simple Customer
Experience
8
Hosting solution
Transcription for
“static” grammar.
Used for ID, numbers,
Yes/No
Transcription of Natural
Language
IVR developer and
integrator
Step Content
Recognition
Technology
2. ID validation Customer identification (policyholder) Nuance (internal)
3. Name &
surname
Declarer identification, often declarer = policyholder, but
some relatives are also allow to declare a claim on behalf of
policyholder
Google Speech to Text
4. Policy
address
Policy location confronted to that in data base records Google Speech to Text
5.Call main
intention
Call intention identification: claim opening, second call
(claim status tracking), complaint,…
Google TTS / MS LUIS
6.Claim
ocurrence date
It needs valid policy for given date and address Nuance (internal)
7.Claim details
Cause and damages identification. sMArt the evaluates if
policyholder has contracted needed guarantees
Google STT/ MS LUIS
10. Contact
details
Telephone number Nuance (internal)
12.VAT
Or other compliance steps if there is anything to be
observed
Nuance (internal)
14. Availability
When claim is stirred to repair or reimbursement needed
info when it is going to be cash settled
Nuance (internal)
Recognition of
intentions expressed in
natural language

9. Connecting dots: matching claim details
with smart contract
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New claim details
Smart Contract
Intent
Entity 1
Entity 2
Entity n
Claim cause
Damage 1
Damage n
Exclusion
Cause Guarantees Limits,…
Claim coverage decision (policy evaluation)

10. Smart Contract: a unique software repository to be
invoked by MAcarena
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From general insurance
conditions...
... to sMArt Contract...
... to Digital Dialogues
creation
Aunique repository of policy conditions, business rules and company instructions

11. Seamless omnichannel experience for customers
based on digital dialogue & smart contract
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USERS
DIALOGUES/
CHANNELS
SMART
CONTRACT
Claim Handler
Agent
Broker
Self Service
Script
Claim Classification
Chatbot
Voicebot
Contracts
Including Macarena to deploy 100% automated FNOL and steering

12. A true companion beating market standards
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Key figures
Customer
completes full
path including
coverage
assessment &
appointment
schedule
9
Clients
30%
Complete
automation
50%
Partial
automation
Macarena gathers some info that is presented to the
agent when interaction is handed over (time saving)
30%
Reduction of
error vs non
digital
40%
Reduction of a
call duration
4.5/5
Satisfaction
index
770K
Calls per
year Recognition of
customer & why is
calling, plus
personalized treatment
Encourages self-
service and transfers
to human agents
efficiently
Steering towards
best possible
solution
Real-time monitoring:
improvement of
service level and
abandon rate
Easy & Flexible
Management:
automated fast track
if desired
Time saved in
personal claim
handling enhances
customer experience
Key benefits

13. ©
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Allianz
Partners
Thank you!