Interested in The Trade Life Cycle?
From the point a securities order is placed in a digital channel, there are several operations applied on the order and these operations depend on the asset you are trading...
This paper will walk through the operations after the securities are placed in the digital channels.
On the completion of settlement and clearing, the buy side or the sell side should have the securities or cash received. But this is not the end of the trade life cycle, especially to the buy side as they are now holding the securities, their corresponding broker and custodian need to manage the ongoing economic, risk, and compliance of the securities. This is the Post-Settlement stage and the following are the key activities in this stage.
In this article, we will be covering the Reconciliation, Reporting and Business Intelligence
Due to the complexity of the trading process, trading data are being sent across the network to different systems and agents. That ends up with the possibility that trading data from different systems in a bank are different. Hence, trading data reconciliation is required between systems in the bank.
The most common reconciliation process is between the client statements system and source systems of the account balance and transactions.
Client statement is critical information sent by the bank to its clients. The accuracy of the information on the statement is regulated by the market regulators, such as MAS and HKMA. Banks are required to make sure the information on the statement is completely correct. However, incidents of incorrect client statements, such as statements containing information from other clients are happening from time to time. These incidents impose serious reputational and financial risks to the bank and need to be managed before the incorrect information reaches the clients of the bank.
To eliminate the incorrect information on the client statement, an end-to-end reconciliation process is required. End-to-end means extracting the data from the generated statements (not from the data in the client statement generation system). In general, these are PDF files. Then reconciling the data against various source systems.
< Figure 1: End-to-End Reconciliation of Bank Statement >
The challenge is the data extraction process as the information on the PDF file is unstructured, transforming this unstructured information to structured information is error prone. One of the solutions is the COMPASS Statement Reconciliation Engine from Axisoft. The solution provides flexible definitions to the client statement, so the data extraction process could be error proof instead of error prone.
< Figure 2: JSON file extracted from Client Statement >
In addition to the comparison of data from different sources, there are several additional considerations in the reconciliation process, such as;
The sequent of reconciliation processes
The frequency of reconciliation processes
The tolerance level of data discrepancies
The time window for the data reconciliation
Data security and privacy consideration
Reconciliation report and dashboard
Data correction and error handling
Reporting and Business Intelligence
In addition to the static reports that are generated in a regular frequency, technology today extends the reporting function to real-time and dynamic analytic and data dashboards, which are usually referred to as business intelligence. It is a process that conducts the real-time analysis of the data from the data warehouse to obtain business insights. So that users can specify the criteria of the data requirements and the reports or analysis could be generated on the fly.
In the trading process, reports could be classified into 5 categories.
Management – For the assessment of business performance and risk level
Regulators – For the fulfillment of business franchise and risk monitoring
Operation department – For the day-to-day business operation
Clients – For the client servicing, sales, and marketing
Trading agents – For operation risk and efficiency management
Regulatory reports are usually static and are generated regularly. The rest could be either static or dynamic using business intelligence.
The core enablement of report generation is a financial data warehouse. It serves as a master data source for reporting and operations of other downstream systems. It contains not just the latest data but also the historical data. These financial data are fed from different sources, such as trading systems, client information systems, back-office systems, etc. This process is referred to as ETL (Extract, Transform, Load), which is a fairly complex process in a large scale data transfer.
< Figure 3: ETL Process >
In the ETL process, data from different sources and in different formats are selected and saved (Extract) into a staging area, which could be a folder in a hard drive or a database. In general, the selection could base on the date and other criteria of the target data source. The staging area is a workspace for the preparation of the data before they are loaded into the data warehouse.
Data in the staging area will go through several processes (Transform), such as
Cleaning – To validate the data and reject the data fail the validation
Conversion – To convert the data to the acceptable format, for example, codebase conversion (EBCDIC to ASCII), precision conversion (integer to decimal).
Classification – To classify the data into different categories. The process could involve the lookup of reference data or external data.
Patching – To fill the value of missing data, such as the default value of the numeric data items or the data process date.
After the completion of the transformation processes, the data are rearranged and loaded to the target tables in the Data Warehouse (Load).
One of the challenges today is the ETL frequency that determines the updated level of the data. Today business is demanding near-time or even real-time information in the reports. However, achieving such requirements requires sophisticated data and technical analysis and design.
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