Transaction Monitoring With Alessa

Alessa is a cloud-based software solution designed to help organizations enhance their compliance to internal controls and regulations or prevent financial crimes like fraud with various capabilities including transaction monitoring, fraud detection and prevention and more.

By integrating with existing corporate finance systems or business core systems, Alessa monitors an organization’s transaction data and using rules, advanced analytics and automation, detects high-risk transactions that require investigation. Once reviewed, flagged transactions can be blocked, assigned to other team members, or approved.

Organizations that use Alessa’s transaction monitoring capabilities include:

• Corporations to ensure compliance to internal controls for social and corporate governance
• Banks, money services businesses (MSBs), credit unions and other financial institutions to review wire transfers and other financial transactions to detect transactions above a certain threshold or suspicious transactions for compliance with anti-money laundering (AML) and counter-terrorist financing (CTF) regulations
• Casinos and other gaming companies to review winnings and other financial transactions by patrons to meet AML/CTF obligations
• Insurance providers to process claims, detect potential fraud and protect from revenue losses

Manage High-Risk Scenarios in Real-time

Alessa accesses transactional data in real-time and/or periodically and feeds it into the analytics platform to detect high-risk scenarios or events. Once detected, the system will allow transactions to be blocked, investigated, or recorded with no action required.

Alessa uses advanced analytics and rules-based scenarios to score the risks associated with suspicious transactions and help fraud and compliance personnel refine and prioritize investigations.

Capabilities

Real-Time, Periodic or Event-Based Monitoring

Monitoring of transactions can be done in real-time, periodically or by specific events. Transactions are sent to an analytics engine where they are reviewed against pre-configured rules or fraud models to look for suspicious transactions. The solution creates alerts for each transaction that requires further investigation and remediation.

Rules-Based Analytics

While Alessa has out-of-the-box rules for specific use cases, these are normally refined based on business requirements. In addition, the rules engine allows organizations to create and deploy their own rules, depending on their specific needs.

Machine Learning and AI

The advanced analytics models include capabilities for anomaly detection, machine learning, and predictive analytics. The models are trained continuously using the business’ transaction history and case management actions. Alessa also allows businesses to deploy their own analytic models.

Workflow and Case Management

Upon identification of a high-risk transaction, businesses can automate investigations using workflow and case management within Alessa. Cases are manually or automatically created and for either individual or group of alerts. Workflows are configurable to be as granular or simple as required. Features include assignments to users/teams, permissions, escalations and root cause indicators.

Risk Scoring

Risk scoring capability calculates the risk associated with a transaction. The calculation of the score depends on configurable risk factors that are weighted depending on the risk appetite of the organization. With risk scoring, investigators can focus on the transactions that pose the highest risk to an organization.

Decision Learning

Alessa can learn how alerts have been actioned in the past against specific transactions and reapply those decisions to future alerts. For example, if wire transfers to specific high-risk countries are always blocked then the system can be “taught” to always make that decision without the need for further human interaction.

Designed to Scale

Using the Microsoft Azure cloud, Alessa can use multiple instances of the screening service, use multiple nodes in order to quickly, and efficiently review millions of transactions. The use of the cloud also reduces infrastructure costs by only deploying the server capacity needed to review current transaction volume.

Managing Suspicious Transactions

Upon identification of a high-risk transaction, businesses can automate investigations using workflow and case management within Alessa. Cases can be created manually or automatically and for either individual or a group of alerts. Workflows are configurable to be as granular or simple as required. Features include assignments to users/teams, permissions, escalations and root cause indicators.

During the remediation process, users transition cases through the workflow. At each transition, different users or groups can be assigned (as established by permissions) and comments and attachments added.

Notifications within the case management module specify which users are notified, how they are notified (email, SMS or in-app), and the content of the notification.

Use Cases

Real-time Wire Transfer Screening

Alessa can be used to screen wire transfers in real-time. When the banking system initiates the request for a wire transfer, Alessa screens the sender and receiver against World-Check data and whether the transfer is to a high-risk jurisdiction.

If the wire transfer is blocked,  a message is returned to the banking system and the status is updated to “At Due Diligence.”

An alert is triggered and routed to the appropriate person(s). If after investigation the decision made is to release the payment, the platform sends a new message to the core banking system to update the status to “Processed”. Alessa relays the request to the payment gateway using the messaging infrastructure.

Health Insurance Claims Monitoring

For the use case where Alessa is used to identify potentially fraudulent insurance claims in real-time, first, the claim management system sends the claims transactions to Alessa.

Alessa then examines the claims using its anomaly detection engine and scores the transaction based on its attributes. If the transaction is considered high-risk then a message is returned to the claims management system, the status is updated to “At Investigations” and an alert is sent to the appropriate person(s).

If after investigation the decision is made to deny the claim, the platform sends a new message to update the status to “Denied.”

To learn more about transaction monitoring with Alessa, download our brochure.