5 questions for… Mathias Schmidt
Every trade is unique – but all trades still have a common ground. What can be drawn from negotiation data and used as insight for the future?
In the trading sector, it is important to be always one step ahead of your competitors. This can be achieved with different strategies. One strategy is to better understand the amount of transaction data. Especially the sell side collects huge amounts of data. Artificial Intelligence gives the opportunity to analyse these data and generate useful conclusions from it. Within the scope of an internal project, we work on how to collect negotiation data, usefully enrich the data and then display the data in a visually well-structured way for the user. In this way the trader can identify chances and avoid errors possibly made with the previously used trading strategies. Our solution provides support and speeds up trading decisions – mentor does not take own decisions but makes human decisions faster and more secure.
How might useful enrichment of trading data be like?
First of all, the pursued goal must be clear. Let me give you an example: I want to increase my turnover with my top 5 customers. Now, I define the ratio between effectively traded volume and the volume requested by the respective customer as Key Performance Indicator (short KPI). Then, I only need the respective connections (API) to the inventory systems so that the indicator platform mentor can help me to aggregate the previous transaction data. In the dashboard, new trading recommendations are promptly displayed in simple and constructive statistics. Based on these recommendations, I can adjust the transaction.
Our solution can be integrated in any bank-internal IT and is, at the same time, also designed for use in the cloud – thanks to open gateways. One advantage of the cloud: updates can be implemented more quickly.
How are the KPIs displayed?
mentor has a web UI in which the defined KPIs can be displayed in tabular or graphical formats. Like this, the user gains a good overview on the current indicators as well as their development over time. It is also possible to display individual trading data regarding a specific transaction. So, a deeper insight in the data volume can be achieved, and relevant conclusions for simplifying future trading processes can be drawn.
Which target group is addressed with such analysis methods?
Generally, every person processing transaction data at any point of the trading workflow is part of the target group – including price calculation, settlement and booking. The trader is able to see which products or instruments are strongly demanded at the moment, and also receives information on the relation to the customer and the experience drawn from previous deals. Thereby, binding knowledge to individuals can be prevented and a knowledge pool can be created instead. Meanwhile, the sales employee can see the performance improvement – not only by means of completion rates and revenues but also based on specific aspects of the trade having received only little attention before. And business analysts can use the enriched data volume for analyses and concepts having an impact on the future processes in banks and trading in general. Overall, KPIs can be better evaluated for supervisors.
What is required on the technical side for a solution like mentor?
This depends on the hosting decision. With the inhouse platform solution, the software is run in the customer’s technical infrastructure. The cloud variant provides the same scope of services; the transaction data is transferred to the cloud via API – of course, the data is encrypted and fully secured so that it does not fall into the wrong hands. With this model, we are responsible for the software operation. New add-ons and updates are also integrated into the cloud in real time.