Given Progress’ traditional approach to leverage partners to embed and sell its products, we should note that to this point Apama sales have been based mostly on a direct sales model. To date, sales have been primarily to banks and other larger companies in financial services, where CEP is deemed as a “bet the farm” solution. Sales into governments, telecommunications, utilities, transportation enterprises and so on will also likely target major enterprises. These operations will require a continued expansion of a direct sales channel as well as partner-based channels for smaller organizations.
One good example of a partner embedding Apama would be Manuvis within its FactoryMRI manufacturing execution system (MES). Manuvis’ system continuously monitors production equipment and other key production statistics in a discrete manufacturing environment (e.g., production of auto parts). Should the software detect a continuing anomaly in a machine, it will notify workers to tend to the machine. The software will also dynamically re-direct production and re-optimize the production schedule.
Generally speaking, CEP tools can link directly into data collection and automation systems sending signals from the production floor, as well as into packaging, warehousing management systems (WMS), and other related business systems to guide problem resolution and improvement. Another example of Apama’s use in the production environment is to detect bottle-filling trending low or high in a large high-speed bottling plant.
By defining threshold and time window limits, the system provides alarms and dashboard visibility on the fly, as well as comparisons over any period in history. Data input can be sensor outputs, control instructions, transactions and so forth, while connection taps can be made into programmable logic controller (PLC) and control system data streams, as necessary.
Progress has been working with several international regulators, including the UK’s Financial Services Authority (FSA, which is the counterpart of the US Securities & Exchange Commission [SEC]), to incorporate real-time fraud detection technologies into its market monitoring endeavors to help detect fraud. One of the drivers for FSA’s SABRE II (Surveillance and Automated Business Reporting Engine) fraud-detecting initiative was for the regulatory authority to become more dynamic and proactive in detecting market abuse. This proactive approach would be achieved by investigating potential offenders more quickly with relevant evidence, and by identifying trading rings and links between individuals generating illicit market impressions.
The idea was to also instantly detect and prosecute “extremely lucky” individuals (e.g., traders who constantly take a best offer in a market to drive up a share price) and monitor for price/volume movements where companies may need to make a disclosure of price sensitive information. The other drive was to implement all requirements for the Markets in Financial Instruments Directive (MiFID) in terms of transactions reports, policy calculations, and inter-regulatory reporting.
There are hundreds of known and possible illegal trading patterns (tricks), but traditional algorithmic techniques have not been able to detect their use in real time. Thus, Progress Apama is used to promptly detect insider trading, breaches of short-selling rules, wash trading, the spreading of illegal rumors followed by suspicious buying patterns, “painting the tape” to drive a stock’s price up, front-running of orders, and trader collusion (with insider knowledge and “agendas” to buy across different trading venues), as well as many other common market abuses.
The point here is to detect fraud while it is occurring (not after the fact), so that regulators can detect market manipulation that is in breach of regulations in a timely manner. Hence, before a trade is placed, real-time rules should detect both illicit doings and honest mistakes (like so-called “fat-finger” errors or decimal points in the wrong places), apply real-time compliance rules, or make sure the firm is not over a certain percentile of an actively traded market.
Given the need for in-depth know-how and domain expertise, Progress has teamed up with specialist consultancy Detica Group PLC (now part of BAE Systems) to encode market knowledge into algorithms that detect illegal trading patterns. Other possible examples of Apama deployments could be: the detection and prevention of credit card fraud (e.g., detecting several same-number credit card transactions at physically distant retail stores in an atypical time bracket); analyzing patterns in passenger movements (e.g., to detect potential terrorists); aviation control; and predicting the best route for vehicles such as tanks and long-haul transport.
One good example of a partner embedding Apama would be Manuvis within its FactoryMRI manufacturing execution system (MES). Manuvis’ system continuously monitors production equipment and other key production statistics in a discrete manufacturing environment (e.g., production of auto parts). Should the software detect a continuing anomaly in a machine, it will notify workers to tend to the machine. The software will also dynamically re-direct production and re-optimize the production schedule.
Generally speaking, CEP tools can link directly into data collection and automation systems sending signals from the production floor, as well as into packaging, warehousing management systems (WMS), and other related business systems to guide problem resolution and improvement. Another example of Apama’s use in the production environment is to detect bottle-filling trending low or high in a large high-speed bottling plant.
By defining threshold and time window limits, the system provides alarms and dashboard visibility on the fly, as well as comparisons over any period in history. Data input can be sensor outputs, control instructions, transactions and so forth, while connection taps can be made into programmable logic controller (PLC) and control system data streams, as necessary.
Progress has been working with several international regulators, including the UK’s Financial Services Authority (FSA, which is the counterpart of the US Securities & Exchange Commission [SEC]), to incorporate real-time fraud detection technologies into its market monitoring endeavors to help detect fraud. One of the drivers for FSA’s SABRE II (Surveillance and Automated Business Reporting Engine) fraud-detecting initiative was for the regulatory authority to become more dynamic and proactive in detecting market abuse. This proactive approach would be achieved by investigating potential offenders more quickly with relevant evidence, and by identifying trading rings and links between individuals generating illicit market impressions.
The idea was to also instantly detect and prosecute “extremely lucky” individuals (e.g., traders who constantly take a best offer in a market to drive up a share price) and monitor for price/volume movements where companies may need to make a disclosure of price sensitive information. The other drive was to implement all requirements for the Markets in Financial Instruments Directive (MiFID) in terms of transactions reports, policy calculations, and inter-regulatory reporting.
There are hundreds of known and possible illegal trading patterns (tricks), but traditional algorithmic techniques have not been able to detect their use in real time. Thus, Progress Apama is used to promptly detect insider trading, breaches of short-selling rules, wash trading, the spreading of illegal rumors followed by suspicious buying patterns, “painting the tape” to drive a stock’s price up, front-running of orders, and trader collusion (with insider knowledge and “agendas” to buy across different trading venues), as well as many other common market abuses.
The point here is to detect fraud while it is occurring (not after the fact), so that regulators can detect market manipulation that is in breach of regulations in a timely manner. Hence, before a trade is placed, real-time rules should detect both illicit doings and honest mistakes (like so-called “fat-finger” errors or decimal points in the wrong places), apply real-time compliance rules, or make sure the firm is not over a certain percentile of an actively traded market.
Given the need for in-depth know-how and domain expertise, Progress has teamed up with specialist consultancy Detica Group PLC (now part of BAE Systems) to encode market knowledge into algorithms that detect illegal trading patterns. Other possible examples of Apama deployments could be: the detection and prevention of credit card fraud (e.g., detecting several same-number credit card transactions at physically distant retail stores in an atypical time bracket); analyzing patterns in passenger movements (e.g., to detect potential terrorists); aviation control; and predicting the best route for vehicles such as tanks and long-haul transport.
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