Abhishek Malik, Consultant, TCS BaNCS for Capital Markets
Abbreviations: CA – Corporate Actions, AC – Announcement, KPI – Key performance indicators, ACU – Announcement capture utility, AI- Artificial intelligence, ML – Machine learning
Even in some of the most direct corporate actions events, there may be a significant amount of cash value involved, especially if dealing with substantial holdings of securities. An event ignored when it should have been acted upon, a missed election deadline, or an entitlement paid to the wrong party are all examples of high-risk corporate action processes that can incur significant losses to the bank and impact its reputation. Announcement processing is the first and one of the most important stages in overall CA processing. Inaccurate or untimely processing of an announcement has a domino effect on the rest of CA lifecycle and presents itself as a high-risk area.
Financial institutions and fintechs continue to devise technological solutions to counter risks associated with announcement processing. One of the solutions is the development and deployment of an announcement capture utility (ACU). An ACU can be deployed by a capital markets organization to create efficient announcement scrubbing and distribution processes that are capable of servicing multiple lines of business e.g., custody, wealth, investment banking and multiple geographies from a single platform. An ACU processes CA event information captured from various channels such as data vendors, custodians, stock exchanges, depositories etc to create a scrubbed golden record. A golden record represents the most accurate CA event information copy. This golden announcement is then distributed to downstream systems such as corporate actions, wealth management and security trading platforms. Deployment of an ACU is aimed at realizing the following system improvements: -
- Generating better quality CA event announcements
- Achieving faster turnaround time of golden records for downstream systems
- Becoming a centralized utility eliminating the need for deploying separate solutions and installations per business unit.
- Enabling cost reduction in terms of software licenses, data vendor costs (by consolidation of subscriptions across different business units), single operations team, and system hardware optimization
- Risk reduction and increased customer satisfaction are among other possible benefits
You can’t manage what you can’t measure. To quantify the effectiveness of an ACU, key performance indicators (KPI) should be established and measured. Measurement of KPIs is critical so that constant health check is performed and the ACU delivers on its promises. Following areas should be quantified and measured with respect to the performance of ACU.
1. Timeliness of Golden Record - Timeliness can be measured as the ACU’s ability to produce golden copy as early as possible in the corporate action event’s lifecycle. An announcement sent much before the record date or response deadline date provides enough processing time to its recipient and helps tap into investment opportunities arising out of the event. Timeliness of the ACU can be determined by comparing the golden copy generation date with other key dates such as ex-dividend date, record date or response deadline date. ACU should target to send all critical announcement updates e.g., payouts, key dates as prior as possible to the record date/ response deadline date.
2. Accuracy and Completeness of Golden Record – Accuracy indicates ACU’s ability to generate the golden copy with as accurate information as possible (close to the issuer version of the event). Completeness indicates presence of all key event attributes such as ex-dividend date, record date, option details, payout rates or response deadline dates without which an announcement cannot be termed as complete. While accuracy of the golden copy is a difficult parameter to measure, it can be derived from the feedback and complaints of the consumer/downstream systems. A pro-active method to measure accuracy could be to analyze the type and frequency of changes being done in event information - If there are frequent updates to key attributes, then reasons for the same should be analyzed e.g., is it the source sending incorrect information or is it a scrubbing issue of the ACU or incorrect manual action from the users etc.
3. Turnaround Time of an Announcement - This pertains to the ability of the ACU to process announcements received from various channels and generate a scrubbed golden copy within an agreed timeframe e.g., four hours from the receipt of announcement to distribution of the event. This can be determined by measuring the time taken by ACU from capture of announcement to generation of golden record. ACU can deploy AI/ML tools to automatically process unstructured announcements (e.g., PDF prospectuses) to reduce the manual effort of the operations team. The focus should also be to process announcement feeds in electronic formats such as ISO 15022/20022 automatically, thus achieving a better turnaround time.
4. Cost Reduction - Bringing in cost efficiency is an important objective for an ACU. This is achieved via consolidation of staffing, IT platforms, data vendor subscriptions and reduction in losses etc. Some examples of ACU cost heads are:
- Staff compensation– Operation users and IT staff
- Infrastructure cost – Production and non-production hardware for ACU deployment
- Software licenses, annual maintenance cost, system enhancements w.r.t. ACU
- Data vendor subscription costs etc.
The focus here should be provided to bring in on-going improvements via AI/ML/RPA technologies. This will help reduce manual work and provide the ability to handle higher event volumes with same setup.
Above mentioned, performance areas of ACU should be collected programmatically at pre-defined frequencies. Descriptive and predictive analytics can help generate insights into the data and metrics from these programs. Some examples of how these metrics can be displayed and analyzed are provided herein:
This chart provides a monthly trend of % announcements meeting timelines and SLAs. In addition to timeliness of ACUs, it should be captured for each data source so well to identify the root cause of delays.
This chart depicts that voluntary and choice events are not meeting the targets of turn-around time.
This chart depicts that average processing time for voluntary and choice events is considerably higher than mandatory.
This chart depicts that a higher number of of voluntary and choice events were incomplete as compared to mandatory events.
Disclaimer: Views or opinions represented in this blog are based on the author’s own research and do not represent TCS BaNCS.