Case Study: Middle Office AI

Challenge

We found many asset managers were locked into rigid and aging middle office reconciliation systems. Because the majority of these systems are simple and don't learn, asset managers can't reduce labor costs.

Our work with several major, global asset managers presented several major challenges:

Rising interest rates have created urgency for more accurate predictions of cash and positions between IBOR and ABOR records.
Failed trades predictions. Most trades are predictably delayed, yet are treated as if they might be failed trades. In rarer cases, investment operations staff might assume a trade is delayed when it has actually failed.
Accurate cash predictions. Penalties for overdraft are now substantial given rising interest rates. It is vital that investment teams have a much more accurate prediction of their daily cash positions, given all other activities in flight.
Reducing ineffective reconciliations and labor. Many teams allow rules-based reconciliations to continue, often not understanding how they relate to one another. Some may be redundant, while others may be less effective at spotting root causes.

Diagram of a position passing through multiple books of record and the reconciliation process.

There is a transaction chain spanning multiple parties and books of record

Reconciliation systems generate too many false positives, keeping labor costs high in investment ops

Solution

Our middle office AI solution provides a platform to rapidly ingest any data from various electronic databases as well as from contracts and PDFs. As illustrated below, each asset class may flow through several machine learning "states." Our algorithm can track the position through each state - like a trip map - and help provide asset managers a more accurate views on their IBOR to ABOR.

Middle office reconciliations must handle records from multiple systems and formats.

How It Works

We help setup a programmable pipeline of all data and document sources first. Then we assist in the development of more sophisticated reconciliations. These often start as incidetn detection checks, but can be integrated to feed more sophisticated predictive algorithms. Finally, we help rigorously test the algorithms. Once the algorithms and checks are operational, we compare them to current reconciliations to gauge the redundancies and help identify recs and labor costs that can be reduced.

Because our method accelerates the managing of different data sources and configuration of reconciliations, we are able to create very sophisticated algorithms such as incident detection checks which are setup to look for the same conditions that caused errors and delays in the past.

Outcomes

There are three major outcomes we have achieved in this financial reporting case.

Faster and more comprehensive data extraction and transformation. Our programmable pipeline can very rapidly connect to different data sources and documents.
More sophisticated reconciliations that are integrated with algorithms. We help design and configure incident detection checks, performance checks, and link them as inputs to more sophisticated algorithms.
Labor reduction resulting from checks and algorithms that lower exception rates. We have a significant track record of radically reducing false positive rates, which drive the volume of most exceptions. Exceptions (false positives) are strongly correlated with labor costs.

Middle Office Contract Confirm Solution

As illustrated in the diagram below, our AI platform leverages both visual and natural language processing algorithms to intelligently read and parse the relevant economic terms and legal language in contracts. Once extracted, our system automatically retrieves the matching trades and electronic records to perform the paper to electronic reconciliation seamlessly.

Solution

As illustrated in the diagram below, our AI platform leverages both visual and natural language processing algorithms to intelligently read and parse the relevant economic terms and legal language in contracts. Once extracted, our system automatically retrieves the matching trades and electronic records to perform the paper to electronic reconciliation seamlessly.

How It Works

Complex contracts are automatically read with our AI using a variety of algorithms. Once this is performed, the system automatically retrieves the matching electronic records. It begins to learn - with human feedback - to improve its matching capabilities.

Outcomes

There a number of major outcomes we have achieved in this complex contract case.

OTC review time reduced 82%. We measured the baseline mean time to manually read a paper confirm and reconcile it against trading records. Our algorithm reduced the labor time to run these complex checks by 82 percent.
Broker/dealer delay transparency. Our system now tracks and provides algorithmic benchmarks for delays in sending and approving confirm contracts across 18 broker/dealers.
Tracking and prediction of disputes, breaks, and root causes. The algorithm keeps track of all disputes and automatically correlates disputes by break type.

Case Study

Middle Office AI

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OnCorps elevates work and decision making by applying groundbreaking artificial intelligence algorithms to labor-intensive operations. Our systems have dramatically reduced labor, risk, and cycle times.