- Case Studies
mentor is an innovative KPI platform evaluating existing trading data in real time and in a structured way. In combination with historical negotiation data, new matchings are created. By using a minimum amount of resources and a target-oriented use of your transaction data, mentor can assist with generating additional business. Simply specify the necessary targets for your business and check the results, for example, new trading suggestions directly via mentor.
This solution processes and usefully enriches a huge amount of negotiation data which is then visually structured and displayed in real time. Thus, it is possible to better identify opportunities, to avoid previous errors in trading strategies and to be able to make better predictions for future trading decisions. Classic dashboards, which usefully display the previously defined key performance indicators (KPI) in a clear overview, can be managed in a simple web UI.
The matching platform goes even further: Buying and selling interests, both own or other interests, as well as historical negotiation data are compared on the basis of similarities, so that potential matchings can be detected. Consequently, mentor provides a database for customer interests, negotiation data and potential matchings. Users are informed in real time and thus effectively supported for increasing sales.
Data as basis for analysis
Different formats of key indicators
“My objective is to increase the proportional customer revenue. For this, I need an appropriate reference value.”
The customer revenue can be determined by calculation of typical Key Performance Indicators (KPI). Moreover, a more detailed analysis of the bank’s proportional revenue in relation to the overall customer revenue is possible. In the end, these are two strong indicators with high significance.
The mentor indicator platform parametrises these indicators and enriches them with additional data such as, for example, asset classes or time intervals. With comprehensive filter options, individual KPIs with even higher informational value can be created. The results are shown via dashboards in a modern web front-end whereby real-time information as well as context-related information, e.g. via customer requests, is updated automatically.
“I have a position for a specific bond and would like to know which customer might be interested in this bond.”
The mentor indicator platform offers a database which can be filtered by a variety of data and structures, e.g. by an instrument book, and thus the top customers for the selected instrument/bond are displayed. The interest is displayed in tabular and graphic form so that the customer having the greatest interest in the position can quickly be identified.
“I have a customer interest for a specific bond and would like to know whether a countertrade is possible.”
In addition to all concluded transactions, mentor also analyses interests and requests which have not been traded yet. Within the database it is always possible to set filters for interests to any specific financial instruments. mentor compares all kinds of positions: banks’ inhouse axes, customer interests or customer positions from historical negotiations. All datasets are reconciled in a structured way based on parametrisable similarities. Any matchings are reported to the interested party in real time. In this way, the trade for the customer’s original interest can be mediated and the countertrade can be concluded at the same time.
“I have a customer interest which does not match any other interest. Are alternatives proposed too?”
The mentor matching platform provides the possibility to filter suitable alternatives for an interest on the basis of defined similarities such as sector, issuer or term buckets. Like this, the customer receives different alternatives for the case that the direct matching – whether an own or other matching – is not suitable.
Product Owner, valantic Financial Services Automation
”Thanks to the aggregation of data which is collected in financial institutions due to regulatory requirements, correlations can be better understood and checked.“
mentor functions as advisor for customer interests and delivers real-time notifications regarding potential matchings. The input of customer interests can be carried out in different automated ways, e.g. via valantic’s icProfit. The modern and open architecture delivers an open standard API from the very start so that customer interests and axes can be delivered to mentor via third-party software such as Refinitiv’s Eikon, Excel or Bloomberg.
Chart for matching and administration of interests as well as transfer to downstream trading systems
valantic Financial Services Automation