Use Cases

Situation: Manual Reconciliation 
To help clients reconcile data from various different data streams. Also, identify outliers that cannot be identified. Two data streams with similar transaction information but data is presented differently and there is no unique identifier.

Solution: DRaaS + SmartJoin 
Multilevel semantic join based on Hadoop i.e. two hoops of jump and then get joins from there. The schema is for ‘MultiJoin’. Configurable rules engine for validation of incoming and outgoing data and create rules for project

Estimated Savings: Approximately 25% resource savings identified as currently, 75+ people are doing this exercise manually

Situation: Federal Bank requirements require account holders data to be correct, complete, standardized, up to date and de duplicated to ensure accurate reporting.

Solution: Used SmartJoin™ + DRaaS to build customer master data. Included data discovery, cleansing and deduplication along with survivorship, manual validation and updating. Added innovations in toolset to adapt to legacy data, systems and quality of information, created rule framework and best practices for governance.

Result: Bank could identify + 99% of unique customers. Helped bank generate high-quality reports, comply with data related regulations. Help embark on a journey of using customer master data for cross-selling and up-selling

Situation: Increase existing customer base while maintaining margins. Data fragmented and of poor quality impedes client service & retention management.

Solution: Hexanika was provided limited data (names, addresses, bank details, product type). Provided geographic profile of customer by breaking up addresses into granular elements and profiling customers as urban or rural and further profiling customers based on neighborhoods within urban agglomerates.

Result: Provided a 52-week deduplication service with zero downtime for the last 6 years while maintaining, managing, and providing customizations for over 70 million records.

Situation: Client’s aggressive growth transformation and execution of strategic roadmap entailing customer, product, and credit re-alignment, process centralization, and technology up-gradation. Required KYC compliance of all accounts across the Bank to prevent freezing of non-compliant accounts; branch rationalization to determine which branches to retain; consolidated view of the customer to migrate data to the new core banking system and for CRM.

Solution: Cleansed, standardized, enriched and deduplicated account data; provided geographical boundaries; created unique customer/household views; classified records on address, contact & identity data; identified accounts for KYC information and developed channel strategy based on addresses/contact data at the household level

Result: Determined channel strategy for data collection and set the stage for bank’s expansion strategy.

Situation: Record linkage for meeting compliance. Identify associated parties with multiple Demat accounts. Multiple Demat accounts were being used for numerous applications resulting in increased chances of allotment of IPO subscriptions

Solution: Hexanika’s expertise, toolsets, and services were used to define linkages and association rules for identifying parties within legacy data. Customer data was matched against the UN sanctions list; adapters were implemented to convert and normalize incoming data formats to standard formats and reject bad data using business rules.

Result: Customized dashboards were created to enable regulatory compliance. Reports were fed back into depository participants (DP) by the client. For specific customers “Know your customer” proofs were provided by DP to complete the cycle

Situation: Existing system for credit screening loan applications against an existing database of 13 million records missed a large number of duplicates. Waste of time and resources expended in manual eyeballing effort. Possible extension of credit to high risk

Solution: Hexanika’s expertise was used to match data and seamlessly integrate with the client’s loan transaction system. Multiple iterations were made to ensure negligible skips compared to the existing system while ensuring maximum relevant matches made against the existing customer database, negative individual database, and negative company database. A flexible rule-based system was implemented for continuous improvement in match quality and there was the flexibility to add new fields for new match rules without IT involvement

Result: Relevant matches increased by 60%

Situation: Identification of key market segments based on profile and potential for cross-selling & up-selling of new policy types.

Solution: Identified distinct customer and agent segments for targeting new products. The process involved: data preparation, data enrichment, data quality assessment, data transformation, exploratory analytics, building analysis tables, cluster optimization, k-clustering trials, shortlisting clustering trial results for policy owners and agents, drill down OLAP analysis & recommendations on which segments to target and which product to target

Result: Distinct clusters identified indicating lifecycle behavior of customers along the value chain. Based on this, the client designed a new marketing & distribution strategy

Situation: Mitigate the risk of bad loans and accompanying possible reputational risk. Client critical requirement was timely and as ‘accurate as possible’ detection of potentially fraudulent applications across personal loans, auto loans, mortgages, secured loans, business loans, etc. Identify with a high level of confidence similarly sounding names & addresses in good turnaround time to enable downstream processes of credit scoring and application approval

Solution: Provided customized ETL to validate, verify and join disparate structures across 7+ types of loan systems in record time; customized middleware to schedule & manage incoming data based on data type and operation; optimal match methodology; and customizable reports of match results for downstream decision making

Result: Client achieved its stated goals in the required time to ensure timely & quality credit screening

Situation: Identification of key market segments based on profile and potential for cross-selling & up-selling of new policy types.

Solution: Identified distinct customer and agent segments for targeting new products. The process involved: data preparation, data enrichment, data quality assessment, data transformation, exploratory analytics, building analysis tables, cluster optimization, k-clustering trials, shortlisting clustering trial results for policy owners and agents, drill down OLAP analysis & recommendations on which segments to target and which product to target

Result: Distinct clusters identified indicating lifecycle behavior of customers along the value chain. Based on this, the client designed a new marketing & distribution strategy

Situation: Identify unique customers and households to increase operational efficiency, cross-selling, and understand cumulative exposure across a single entity. Identify additional duplicates not detected by the in-house IT team. Data spread over urban/rural areas; data quality poor and with variations; and large percentages of matches missed using internal

Solution: Defined various linkages and association rules for identifying unique customers and households. Used cleansing and de-duplication and supported by expert eyeballing services to identify outliers. Provided client with a set of high and low confidence matches for final certification and upload.

Result: The client achieved its stated target of identifying the unique customers and households and achieved a better understanding of the total number of policies held by each individual and household

Situation: Client converting from finance company into a commercial bank. Data from Group’s 8 different legal entities required to be merged into a single cleansed and unique

Solution: Provided expertise in working with data-related issues to identify problem sources and fixes. Identified data-related issues, anomalies, and exceptions in the data and resolved them. Customized rules generated and provided for various types of data- depending on customer type (corporate or individual), customer location, and customer address. Incremental de duplication round to match new records added in interim while cleansing process going on with already matched data.

Result: Client achieved its stated goals in the required time-merging various databases into a single entity.