Every day, financial crime investigators face thousands of alerts and it can take hours to understand how much risk each alert actually presents
Conducting manual searches across disconnected systems and data to identify the cause of an alert is not only inefficient but makes it difficult to actually identify criminal activity in a mass of false positives.
Generative artificial intelligence (AI) is changing the game, helping financial institutions achieve higher quality investigations in a fraction of the time. McKinsey expects financial services to see the largest gain from generative AI adoption – primarily through increased productivity – with the potential to deliver $200-340 billion in annual value (equivalent to 9-15 percent of operating profits).1
The Sensa Copilot generative AI assistant is proven to accelerate financial crime investigations by 70 percent, helping investigators to leverage data more accurately and with less effort through the use of natural language queries. As part of the broader Sensa Investigation Hub, Sensa Copilot leverages industry-leading Microsoft Azure OpenAI to automate and accelerate tasks that are typically manual and time intensive.
Let’s take a look at four key functions where the Sensa Copilot can enrich the investigative process
#1. Summarization and transcription
Sensa Copilot collects, analyzes, and summarizes all of the available data and documentation at scale, generating easy to understand, insightful narratives and case notes.
#2. Data visualization
Sensa Copilot provides intuitive natural language querying of large and complex datasets – investigators simply ask a question and the Copilot will deliver easily consumable, visual results in seconds, saving investigators hours of manual calculation and theorizing.
#3. Augmented web searches
Sensa Copilot enables curated searches to open web sources, pulling back data and delivering auto-generated summaries for each search result. The ability to search across multiple languages and translate results automatically expands the depth of information that can be included and saved in the investigation with no additional effort.
#4. Recommended actions
Sensa Copilot helps investigators easily see what actions or steps of the investigation are completed or still required, specific to the type of investigation being conducted, and easily take the next steps.
Helping assist investigators every step of the way, Sensa Copilot ensures nothing is missed and risk is effectively assessed, all while dramatically improving investigative efficiency and anti-financial crime outcomes.
Learn more about Sensa Investigation Hub and Sensa Copilot by SymphonyAI
These tools on Microsoft Azure help you reduce the complexities and inefficiencies of financial crime investigation.
Generative AI for financial investigations FAQs
AI, or artificial intelligence, refers to the broad field of technology that enables machines to perform tasks requiring human-like intelligence, such as learning, reasoning, and problem-solving. Generative AI is a subset of AI and specifically focuses on creating content, such as text, images, or music, by learning from existing data and generating new, similar outputs.
Generative AI in AML can be used to simulate and model potential money laundering scenarios, helping to identify complex and emerging patterns that traditional systems might miss. By generating synthetic data, it can also assist in training and improving the accuracy of machine learning models used for detecting suspicious activities.
Generative AI in finance can be used to automate content creation, such as generating financial reports, forecasts, and personalized investment strategies. It can also assist in fraud detection, risk assessment, and customer support by synthesizing large volumes of data into actionable insights.
Yes, AI technologies are employed to detect money laundering by analyzing transaction patterns, identifying anomalies, and flagging suspicious activities. These systems use machine learning algorithms to improve detection accuracy and adapt to evolving money laundering tactics. Examples include NetReveal Transaction Monitoring and SensaAI for AML.