AWS Financial Services India

SBI Life Insurance

Customer Service Automation · Enterprise Knowledge Search

SBI Life Insurance, an SBI Insurance Group company, provides consumers with products such as an affordable mortality insurance plan, which allows customers to revise the sum insured based on their stage of life; a disability insurance plan that features multiple coverable risks; and a medical insurance plan that covers from inpatient treatment to home care.

It also shortened the training periods by about 30 percent.
SBI Life Insurance develops a document search solution with Amazon Kendra and generative AI, and streamlines call centers with a selfbot Key metrics months to develop the document...

Reported impact

Business outcome

SBI Life Insurance conducted trials with a small group of operators in July 2023. As of 2024, almost all operators use the new document search solution. In the past, the operators had to search across various types of documents to respond to customer inquiries, but the new search system now empowers them to answer quickly.

  • Customer Experience
  • Productivity Improvement

Solution

SBI Life Insurance is responsible for the life insurance services of the SBI Insurance Group . The company creates and delivers insurance products that match modern needs. Using generative artificial intelligence (AI) capabilities from Amazon Web Services (AWS), the company built a document search solution to retrieve procedural documents on their insurance products and policies, launching the solution at its call centers. The solution also features a selfbot for quicker customer support and faster operator training. To help solve these challenges, the Information Systems Department started building a solution that let call center workers easily search for documentation on insurance products and policies.

Data flow

Amazon Kendra uses natural language processing technology to read unstructured data in PDF files as a source.

Solution architecture

4 components · 3 layers
  1. Compute
    • Amazon Bedrock Runs the foundation model layer for the knowledge search and question-answering workflows.
  2. Storage
    • Amazon S3 Stores source data, documents, or generated artifacts used by the knowledge search and question-answering workflows.
  3. Serving
    • Amazon Kendra Indexes enterprise content and retrieves source material for the knowledge search and question-answering workflows.
    • Amazon OpenSearch Service Indexes content and powers search or analytics for the knowledge search and question-answering workflows.

Architecture clues

  • "However, adding these functions to Amazon OpenSearch Service would have increased development workloads and impacted the project timeline." In February 2023, Amazon Kendra, an intelligent enterprise...

Evidence from the source

"However, adding these functions to Amazon OpenSearch Service would have increased development workloads and impacted the project timeline." In February 2023, Amazon Kendra, an intelligent enterprise search service, became generally...
Amazon Kendra provides the flexibility to search for information in structured and unstructured content that is scattered across multiple locations, easily bringing convenient semantic searches to users who don't have specialized machine...
Initially, the team considered using Amazon OpenSearch Service , a full-text search and analysis engine.
The Information Systems Department decided to build a document search system using Amazon Kendra at its core.
Using generative artificial intelligence (AI) capabilities from Amazon Web Services (AWS), the company built a document search solution to retrieve procedural documents on their insurance products and policies, launching the solution at...
We therefore decided to migrate our applications to a new technology platform using AWS." SBI Life Insurance needed to enhance customer service at its call centers and shorten the training period for operators.
Transparency Evidence & methodology 61/100 reference value · 85% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value61/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact0/25
    0 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence85%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    6 direct source quotes retained.
  • Date provenance8/10
    The publication date comes from publisher-controlled metadata.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity8/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. AWS

    SBI Life Insurance customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/sbi-life-case-study/
    Source type
    Vendor-published customer story
    Published
    Sep 1, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.