Data science and AI transform disclosure and reporting
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.
One of the world's leading bank
Improve efficiency in disclosure and reporting
Tools and technologies
Python – SciPy, Pytesseract, NumPy, Statistics
The client relies upon a centralized operations team to produce monthly NAV (Net Asset Value) 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.
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.
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.
Anti-money laundering: managing regulatory risks
Data & Analytics, Risk & Compliance
Anti-money laundering programs: managing data, regulatory risks
A global multinational bank successfully managed large data volumes in its anti-money laundering program and protected clients and franchisees from regulatory risks.
A leading multinational bank.
Identify and mitigate risks related to anti-money laundering (AML) regulations.
Tools and technologies
Cloudera, Hadoop, Talend, Spark MLlib, MicroStrategy, Datameer and Sqoop.
Our client, a multinational bank, had a comprehensive global program for anti-money laundering (AML) to protect its clients and franchisees from the risks of money laundering, terrorist financing and other financial crimes.
To be able to do so effectively, they needed to deal with mounting volumes of data, which their existing applications could not handle. Their systems also generated a high number of false positives that increased the need for manual intervention.
We worked with the client’s global anti-money laundering program to develop a solution that provided them with consistent controls to identify AML risks and comply with relevant laws.
We incorporated a modern data lake architecture, a centralized data hub that allowed the processing of increasing volumes of data from around the world. The solution we built was capable of handling data in petabytes.
We helped the multinational bank build a data lake that could hold 8 petabytes of data, much more than its existing data applications allowed. Next, Iris cross-trained the client’s global anti-money laundering team to ensure efficient use of the data lake in line with its global anti-money laundering program.
Global regions covered