Hexanika wins the TiE50 Top Start-up Award at TiEcon 2016 event in Silicon Valley
Hexanika nominated for “2015 TiE50 TOP START-UP”
Hexanika nominated for “Top 100 Promising Big Data Companies” by CIO journal
Hexanika nominated in “500I500 Upcoming Start Ups” by Inc magazine
Hexanika nominated for “Top 100 Promising Big Data Companies” by CIO journal
Hexanika nominated in “500I500 Upcoming Start Ups” by Inc magazine

Products: Ready To Go!

  • Ready to Go – Zero Capex and Low Opex model
  • End-to-end product Solutions that uses scalable computing, distributed computing and parallel processing (Big Data)
  • Adaptable products which complement existing solutions

Solutions: Results Delivered!

  • Industry Focus: Solutions focused for banking industry
  • Unique business model that utilizes cost effective technology and human resources to deliver results
  • Fees based on results delivery
big data banking
Big Data Governance

Converging & Unifying Data from Multiple Sources

Unique solution based on Hadoop framework for integrating, consolidating, aggregating and managing structured or unstructured data from disparate sources.

Regulatory Solutions

Revolutionary tool for Regulatory Reporting

Innovative solution for the unprecedented challenges faced by US banks in today’s ever-changing banking industry & regulatory environment.

Solutions

draas
  • Data Governance Solution
  • End to end ‘data readiness service’
  • Use of advanced algorithms & machine learning
raas
  • End-to-End regulatory reporting solution
  • Customizable  and configurable reports
  • Auto updates as per compliance requirements

Use Cases

Draas

Challenge

Hexanika
Solution

frs

Regulatory
Reporting

Evolving bank
regulations

  • Data profiling
  • XBRL reporting
  • Data normalization
  • Data consolidation
bank

Anti Money
Laundering

Automate processes
and utilize Big Data to
comply with AML

  • Real-time screening
  • Client on boarding
  • Reduce false positives
financial-services

Market
Segmentation

Customer
Segmentation and
Customer Relationship

  • K-Clustering
  • Single view of customer
  • Behavior Drivers Analytics
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Fraud
Investigation

Fines and
Penalties

  • Real-time Deduplication
  • Internal Audit
rbox

Product Specific
(mortgage, credit etc)

Sanitize data to get
one view of customer
across multichannel

  • Data Quality
  • Data Integration
  • Data Profiling
  • Data Mining
COMPANY

NAME: Global Banking & Financial Services Company
SIZE: 100,000 employees in 70 countries
INDUSTRY: Financial Services

 

CHALLENGES:
  • Problems in reconciliation of same transaction being passed over and presented in multiple data streams
  • Analysts were manually identifying duplicate and inconsistent entries in excel spreadsheets of security sales data.
  • Data lacked standard naming conventions and other attributes like client names, status, unique ID

Help client reconcile data from various different data streams and identify outliers which cannot be identified. Two data streams with similar transaction information but data is presented differently and there is no unique identifier.

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

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

Blog

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Why is Regulatory Reporting Tough?

‘Regulatory reporting’ is the submission of raw or summary data needed by regulators to evaluate a bank’s operations and its overall health, thereby determining the status of compliance with applicable regulatory provisions. Governments across the world give prime importance to keep their banking systems updated. This has proved to be an important task, more so after the…

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Scope of Data Integration

Data[1] integration involves combining data residing in different sources and providing users with a unified view of data. This process becomes significant in a variety of situations, which include both commercial (when two similar companies need to merge their databases) and scientific (combining research results from different bioinformatics repositories) domains. Data integration has increased  as the volume and the need to…