Data Extraction and Analytics from PDF Documents

Problem Statement

Non standard ever changing incoming PDF instruction/document formats makes OCR based reading almost useless. We receive/deal with millions of PDF documents daily, it is a fundamental challenge to deliver best quality high speed customer service.

Expected Solution

1. Use of Machine Learning (& NLP, semantics) to extract data fields from PDF Files along with API to push info into core system.

2. A utility to read and ingest any PDF documents with minimum training effort with self service and rapid deployment capabilities.

3. Solution should be able to process readable and non-readable PDFs.

4. Solution should be able read key elements of standardized templates e.g., invoices, agreements, contracts etc.

Integrated Personal Financial Services Platform

Problem Statement

Bundled product across common man needs covering home, auto, health, protection, retirement, & wealth management

Expected Solution

An ecosystem that offers flexibility across the insurance & financial needs for any given individual. This will be a platform collaborating with different organisations covering P&C insurance, Health insurance, Life & Annuity insurance, wealth management company, Home & Auto repairs, medical support providers, etc. It will be an end-to-end platform for identification of leads, to sales & servicing the needs of given individual. All data related to interactions are captured on this ecosystem & utilised for enhanced Total Experiences.

Omnichannel Customer Engagement

Problem Statement

It's difficult to track customer's communication with the organisation, especially with multiple channels.

Expected Solution

Solution should contain pluggable integrator so that post sales, applications will get data from the omnichannel customer interaction (WhatsApp, Social Media, Phone, email etc.) to be used for further processing such as get insights of key issues, identify cross sell and up sell opportunities, and a unified view of all communications. Solution is needed for B2C and B2B customers

Data Intelligence Platform

Problem Statement

External Data Flow - Data shared between various financial institutions (Regulators, Investment Managers, Prime Broker, custodians) needs to be automated. Currently there are multiple sources via data is shared. E.g. Emails & websites. It becomes difficult to track and procure required data through such channels. Data sources can be:

1. APIs,
2. Emails, PDFs
3. Flat files
4. Web scraping
5. Social Media and more

Expected Solution

A capability to accept/ingest data (instructions) from diverse channels and partners into a singular intelligent platform and integrate seamlessly with inhouse core platforms. Solution needs to include capabilities for analysing the ingested data.

Predictive Analytics for Customer Retention

Problem Statement

Banking: Non-payment of loan installments by Retail and B2B Custody Banking customers could be an indicator of loan default. In either scenario, (i) Bank NPA increases and (ii)increases cost of operations
Insurance:
Non-payment of premium installments by insureds could be an indicator of (i) intent to no-renew policies or (ii) callous premium payment behavior to risk losing insurance coverage. In either scenario, (i) carrier loses revenue and (ii)increases cost of operations

Expected Solution

Solution for customer data or behavior anayltics to:
Banking:
1. Predict and mitigate risk of NPAs
2. B2B Custody Banking
3. Ensuring customer retention in case of B2B Custodian customers
Insurance:
1. Mitigate risk of losing insureds to competitions and
2. Avoid lapsing policies.

Employee onboarding

Problem Statement

1. Hiring and Recruitment process is increasingly becoming complex with lack of talent in specific skill areas. Traditional process does not fulfill the nuanced approach required for the current scenario
2. Employee Onboarding process can be deterimental if it leads to a bad experience for the candidates. Issues such as trusted background checks, candidate evaluation, clarity of process for the candidates need to be addressed.
3. Mitigate the issue of increasing no show on date of joining

Expected Solution

1. HR solutions for improving the hiring and recruitment process.
2. Solutions for a seamless emplyee onboarding experience.
3. An AI platform that can help with predictive scoring models for potential employees, similar to CIBIL scores. This can then be used as a surrogate to determine the probability of a candidate joining on the agreed date thereby helping decide on whether to roll out offer or not