ROBOLITICS™, TRANSACTION MONITORING FOR ALL
Robolitics™ is real-time Monitoring, Alerting & Reporting platform providing cross-asset class and cross-regulation surveillance, integrating them into a single platform. This platform has an extensive array of features that makes it a powerful product for a broad range of businesses. From real-time transaction monitoring to alert and case management, Robolitics™ can be meet your specific needs.
Robolitics™’ advanced “Streaming Analytics Engine” combines traditional rule set analysis (our rules are easier to understand and adjust) with decision support tools using Artificial Intelligence and Machine Learning to help you focus on the real risk in the business.
- Artificial Intelligence - monitors alerts and pre-classifies them by “next best action”, such that multiple alerts can be managed faster
- Machine Learning - enables you to reduce repetitive false positives. The machine learning model learns on the job, developing an instinct on how best to address an issue.
This removes the need to revert scenario tuning to the IT team. As a result, Robolitics™ reduces false positives and shortens case processing time, thus improving the productivity of the compliance team. This allows more time to address real issues and operate more strategically.
Streaming Analytics is the core processing component of the platform; it consists of top-level algorithms (Algos) which identify transaction or trade malfesance. A tuned or configured version of an Algo is called a Robot. These Robots focus on identify specific behavioral patterns aligned to the nature of your business.
Robots can be configured to run in real-time in an event driven mode or at scheduled times. New Algos and Robots can be created using the Robot Development Kit (RDK).
Tuning and configuring a robot can be performed by compliance staff using the GUI to interactively explore the impact of changes. These changes can then be passed to a super user for authorisation of any changes. Note: all changes are audited and recorded within Robolitics™.
In addition to event-based monitoring, Robolitics™ can be connected to Swift gateway to provide real-time monitoring and screening of inbound or outbound payments against PEP, sanction and internal black or white lists.
If a transaction triggers an alert, Robolitics™ can pass this to the compliance team for authorisation prior to the transaction being completed, thus enabling them to review the transaction and either white of blacklist the transaction and / or the counterparty.
This core functionality of the platform enables the compliance team to manage and process alerts, supported by a comprehensive set of data:
- Client profile data
- Transactional events
- Historic alerts, issues and outcomes
Robolitics™ Alert Management provides a true 3600 view of the customer, enabling cases to be closed faster and more consistently. The case management tool can also be configured with context-driven checklists to ensure the analyst follows process and policy. It tracks who is responsible for the case, what actions have been done so far, and what is the current status. This way, everyone is clear how the team is progressing.
Critically, the alert management process is where Robolitics™ applies the AI and Machine Learning to alerts to identify patterns and outcomes.
- Explore links between alerts and outcomes, using our algorithmic AI to identify similar alerts
- Our Next Best Action tool will prioritise the alert based on business risk
- Identify quickly the most likely outcome (measured as a probability that the alert fits one of a number of probable outcomes), accelerating the ability to investigate and manage the alert.
These AI and Next Best Action tools enable the business to make step changes in team productivity.
The Alert Management component is also available separately to users of Actimise, Mantis and SAS platforms. It acts as an overlay tool to reduce false positive alerts; in this configuration, the platform is marketed as AlertMiner.
AlertMiner is made up of two core components: Alert Lake which is the pool of alerts that are clustered into cases and Radars that are a series of decision support and tuning tools for identifying the risks associated to an alert and in some case categorising or preprocessing the alerts.
AlertMiner manages the AI, Machine Learning and clustering capability in the platform.
It enables your analysts to explore the links between alerts and cases and using our algorithmic AI, identify similar or linked alerts and suggests how best to manage the alert.
The Robolitics™ Reference Data Lake is the repository of all client account and transaction data. It is the core data against which the streaming analytics engine works when an event triggers a Robot to monitor an event against historic transactional activity.
It is also the source of information surfaced to the compliance team during the case management to identify potential patterns of behavior removing the need to switch between systems during the investigations process.
Robolitics™ has a Graphic User Interface which surfaces large amounts of data in a digestible manner, helping operatives and management focus on the principle business risks and escalate and report STR’s.
Robolitics™ has a number of dashboard widgets to help management and the compliance team members manage alerts and cases according to their risk and priority. The dashboard can be configured for each user or category of user: executives, managers, senior and junior compliance officers, as well as for other teams, such as customer account managers who may occasionally be required to access and use the system.
Combining the power of Robolitics™ and Heliocor’s KYC, Onboarding, and Customer Lifecycle Management platform, Dokstor, provides a single integrated solution for behavioural monitoring and alert management.
Many AML alerts require the customers to supply supporting evidence associated with the transaction. Combining alert and case management with the Dokstor platform enables you to automate the process of requesting supportive evidence directly from the customer and to integrate this evidence automatically into the case as part of the investigation process.
Additionally, alert outcomes can be fed back into Dokstor to ensure that the client risk rating remains current, and if it crosses a threshold, the risk type can be modified appropriately (e.g. change from CDD to EDD), thus automatically managing the data and document held on the customer.