ABOUT COMPANY

Founded in New York by veterans of the banking industry our mission is to provide machine learning-based technology solutions that push automation and straight-through processing to the next level, driving down the need for tedious manual work and freeing up the most precious resource an organization has: its people.

Our flagship product is our groundbreaking machine learning based match rate booster for reducing the manual effort needed for transaction reconciliations.

PROJECT SCOPE

Problem Background:

AI and machine learning are making more inroads into the financial services industry. Beyond the established use of AI for fraud detection and anti-money laundering, the use of AI and ML continues to push into other business cases, including the use for bank back office processes such as transaction reconciliations used for financial control and reporting. For example, those performed for matching bank transactions to business activities such as expenses, invoices or trading.

One of the difficulties that customers have in evaluating such ML based products is that the business value of these products is defined by the efficacy and accuracy of the ML model rather than being based on the particular product features (as it is for standard procedural software systems).

Traditional research / product advisory firms such as Gartner, Celent, EY, Accenture etc create feature based vendor product evaluation reports, but none as yet provide any quantitative measurement of the ML functionality in vendor products.

This project aims to setup the first industry benchmark for this ubiquitous business problem.

Project Goal:

• Take existing Operartis data sets and clean and anonymize these for external use.
• Formally document these datasets such that external parties can utilize the datasets and set them up in their reconciliation engines.
• Create tools to show the match rate, accuracy and reliability curves and design other repeatable metrics in line with benchmark best practices.

Optional additional goals:

• Collaborate with advisory firms to help execute the benchmarks for different customers.
• Assist with outreach to leading vendors for reconciliation engines (Operartis will provide contact details for vendors)
• (Optional) Create a consortium of the leading vendors to help design the different data sets and benchmarks.
• Where possible evaluate the market leading reconciliation engines on these data sets.
• Generate a report on the findings.

Data set sources include:

• Operartis example data sets.
• Simulated data generation (Operartis has an existing data simulator which can be used to generate a variety of test data sets.)
• Other vendor data sets.
• https://fintechsandbox.org Real world financial data sets
• Internal organization data sets (e.g. from your university)

You will work with the core Operartis team and meet weekly with the CEO as the project progresses and will be able to adapt, augment this general plan as the project progresses. The Operartis team is a compact, friendly team where you will have a welcoming place to express your creativity and problem solving skills.