Nephelai is an answer to booking errors on orders and financial transactions, which incurs financial
losses and overwhelm Middle Office teams.
Our solution drastically reduces its consequences by identifying them directly at the moment
of capture in the booking tool and while it is being processed by operations.
Our solution assists three user profiles :
Our assistance solution facilitate order and transaction capture by traders, allowing to detect booking errors before they propagate through the processing chain and improve the quality of data submitted to the Middle Office.
Our solution assist Middle Office at the validation stage thanks to several risk indicator to identify at a glance suspiscious fields. Furthermore, our solution is an additional safety net on electronic transactions.
Nephelai produces activity monitoring report for the chief of operation, who can visualize in real time volume of transactions, error rates, alert not yet addressed by the team and anticipate pic of activity.
Nephelai is a human adventure, carried by a team of engineers passionate about finance and computer science. We have been working for three years on the development of statistical learning algorithms specifically for the detection of booking errors in financial transactions. Our solution relies on modern technology components, to warrant security, robustess and high performance while having a limited impact on your own infrastructure.
Supervised learning uses historical transactions and the knowledge that some of them were wrongly booked to create statistical models which will is then used to determine whether new transactions are correct or not. This approach is particularly useful to detect frequent booking errors, like when a trader tends to mix up 2 subsidiaries of the same counterparty with similar names.
Unsupervised algorithms learn from historical transactions as well, but this time using only the validated version of them. Transactions are clustered into groups of similar objects. When a new transaction is captured, it is compared to the various groups and if it does not belong to any of them it is classified as an anomaly, which may be a booking error or simply a rare event. This approach allows the detection of fat fingers, like an error on the direction of an FX transaction.
Out solution is able to perform spectacularily because all the ingredients necessary to a successful machine learning project are there.
Machine learning is the art of finding statistical schemas within the data observed in the past to anticpate future evolutions. It requires three set of skills to make it happen successfully: competencies in applied mathematics, in system architecture and deep business knowledge. The first is obvious, the second is key to orchestrate ressource consuming algorithms and the last is key because only the confrontation between the data and the business reality allows to extract its very substance. It is because our team gathers all these competencies that this product is so successful..
Generic algorithms work well on a school use case but suffer once confronted with a real use case. That's why we have been working for several years now on algorithms specifically adapted and created to solve that one problem which result in much better error detection scores than generic algorithms.
In addition to a large set of data available for the machine to learn from, Machine Learning requires the existence of statistical schemas, which can be translated as user habits. It happens that markets have their structure and traders have their habits.
Result precision is greatly improved when data are structured, which is the case of financial transactions. The audit trail of each transaction is necessary to the application of supervised learning algorithms.
It is obvious that statistical learning is more relevant when the quantity of data available is large. However, what our algorithms discerns are habits of trader and structure of markets. If these are little diverse, a few dozen past transactions are enough to get reliable predictions.
Nephelai is the association of 5 experienced and passionnate engineers around an idea carried by Eleonore de Vial. When she was a consultant in charge of implementing Middle/Back Office solutions, Eleonore had been in contact with many Middle Office team of banks and asset managers.
The assessment is simple, the majority of tasks accomplished by Middle Office teams revolve around management of errors, either directly spending time correcting them or upstream, their identification via numerous reconciliation process (confirmation, cash, position and trade reconciliation, valuation control, performance control..). and while each software has its own set of controls and reconciliation functions, which themselves requires time consuming set up, they end up being like butterfly net letting many errors going through unoticed for a long time.
This experience, associated with the emergence of machine learning in related areas is born the founding idea of Nephelai.
The founding team of Nephelai is composed of 5 associates with diverse competencies and experiences.
We cumulate in particular 35 years in financial software creation, creation of a former successful startup, 3 years study of mathematic applied to the error detection problem and advance knowledge of financial markets.
Chief Executive Officer
Chief Science Officer
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