A multinational bank leveraged data automation to achieve major gains in reporting efficiency, with 99% accuracy in processing variable inputs, for its global investment fund.
Client
One of the world's leading bank
Goal
Improve efficiency in disclosure and reporting
Tools and Technologies
Python – SciPy, Pytesseract, NumPy, Statistics
Business Challenge
The client relies upon a centralized operations team to produce monthly net asset value (NAV) and other financial reports for its international hedge funds — from data contained in 2,300 separate monthly investment fund performance reports. With batch receipts of rarely consistent file formats — PDF, Excel, emails, and images — the process to read each report, capture key info, and create and distribute new metrics using the bank’s traditional tools and systems was highly manual, time-consuming, error-prone, and costly.
Solution
Iris developed a Data Science solution that rapidly and accurately extracts tabular data from thousands of variable file documents. Using a statistical, AI-based algorithm featuring unsupervised learning, it auto-detects, construes, and resolves issues for every data point, configuration, and value. Complex inputs are calculated, consolidated, and mapped as per predefined templates and downstream business needs, efficiently generating numerous, distinct, and required period-end financial disclosures.
Outcomes
The high solution accuracy helped the client’s global NAV reporting team significantly improve precision, efficiency, quality, turnaround time, and flexibility. The delivered solution contributed to:
- 90 - 95% reduction in operational efforts
- 99% accuracy in processing variable inputs
- Zero rework effort and cost
Our highly customizable and scalable solution can be seamlessly integrated with existing reporting applications and MS Outlook while accommodating additional volumes, report types, and business units.
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Data consolidation speeds up drug search
An automated cloud-based consolidation application for R&D data helped a pharmaceutical company improve turnaround time to create Approval for Product Release (APR) documents.
Client
A U.S.-based pharmaceutical multinational corporation
Goal
Reduce turnaround time for APRs
Tools and Technologies
Amazon’s AWS OPCx, Webmethods, natural language processing (NLP), neural networks, and Python programming
Business Challenge
In pharmaceutical R&D, data is generated from several sources: the process, patients, retailers, and caregivers, among others. Pharmaceutical R&D organizations that use the traditional way of creating APRs manually consolidate paper specifications into binders across all R&D functions.
Specific regional rules, compliance mandates, and external regulations were slowing down the client’s workflow. Many spreadsheets in multiple formats were leading to errors from manual entry and duplication of data — the inevitable “swivel effect” that results from data being pulled out from disparate, unconnected software packages.
Iris was approached to improve the process of collecting and using data from multiple sources; the improvement would help the client identify and develop new potential drug candidates faster.
Solution
Iris’s team of 12 specialists designed, developed, tested, and deployed a cloud-based application that integrates data from multiple regions and eight different systems into a single, unified interface for the client’s users. Our application unified the creation and management of the client’s workflows across its lines of business and 20 different product families.
The development environment included Amazon’s AWS OPCx, Webmethods, natural language processing (NLP), neural networks, and Python programming.
Outcomes
Within a year of the application’s release, 2,800 users were using the application, with 55% of APRs turning around in 10 calendar weeks or less. Thanks to the in-memory data grid, the response time of transactions across the board has been brought down to nearly 2 seconds.
The cloud-based application developed by Iris ensures that data is automatically and seamlessly shared between systems that were previously stand-alone and required the tedious manual entry of data.
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A playbook for banks on managing M&A integration
Efficient management of the complexities of disparate systems and data after merger and acquisition (M&A) integration saves time and money.
Client
Banks that have merged or acquired new businesses.
Goal
Manage migration and integration complexity post M&A.
Tools and Technologies
The Iris business acquisition playbook for banks.
Business Challenge
In a low-interest rate regime, achieving scale is the only way for banks to stay profitable. The top 25 banks are growing at a rate faster than rest of the pack. The search for profitability from scale is predicated upon their ability to ensure that operational costs do not grow linearly with business. A significant part of this growth will come inorganically.
Apart from M&As, brownfield expansion comes with banks selling off their books of business for reasons ranging from realigned strategic priorities to the more mundane need of raising cash. Any IT costs in absorbing the new book of work will negate the advantages of size.
Solution
Iris created a business acquisition playbook for our banking clients outlining steps to insource with a migration and integration strategy. We defined insourcing steps for business and technology teams and created a migration strategy with quantifiable recommendations and a reusable checklist for insourcing activities.
Our solutions enabled clients to deal with post-merger integrations and create a single source of truth for transactional data and positions. We consolidated multiple acquisition playbooks and created a single standardized framework for their lending business. The solution also included data integration management and ensured connectivity for the lending business. The solutions were specifically tailored for applications in the loan origination and servicing space.
Outcomes
Our solution rendered several significant benefits and helped clients:
- Achieve 50% savings in cycle time and cost for post-merger integration of business processes, application, and data
- Capability and readiness assessment and assistance in choosing from insourcing options
- Achieve full migration of data and systems
- Achieve partial migration of systems and data migration and integration
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