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In a highly regulated industry like Asset Management, it is unsurprising to see high levels of repetitive manual work, leading to a large administrative burden, slow processes, and long turnaround times for clients.
But it’s not just the regulations themselves that cause this, it’s the complexity of the products, transactions, customer relationships, and underlying technology that exacerbate the problem and create additional challenges. Legacy technology, disconnected system,s and data silos are the norm, but many organizations are using a combination of automation, AI and modern applications to drive digital transformation. AI is the newest of these elements, and its potential for asset management firms is significant.
In Asset Management and other financial services, day-to-day operations often involve long and complex documents. These documents are essential for client onboarding, managing trading approvals, and meeting compliance requirements. But they take a lot of time to review. Examples include client agreements, compliance and regulatory reporting, fund prospectuses, policies, client reporting, risk management documents, and more.
By leveraging an AI-enabled automation platform asset managers can orchestrate the intelligent use of documents in processes across teams and technologies. In the case of Bizagi, asset managers can also use AI Agents to securely analyze and summarise internal files through the Azure Private OpenAI Service, without publicly sharing any data outside of the organization. With this companies can unlock a whole new level of productivity. One European Asset Management leader is already doing this right now.
AI can be used in various ways to enhance asset management, including:
Portfolio Enhancement: AI algorithms can sift through extensive data sets to optimize investment portfolios while considering factors such as risk tolerance, market dynamics, and investor interests.
Risk Assessment: AI models can anticipate market shifts, pinpoint potential threats, and recommend hedging strategies, helping asset managers with real-time decision-making to reduce risks.
Trading Tactics: AI-driven trading algorithms can perform transactions quickly using current market data and historical trends, refining trading tactics, and improving liquidity management.
Forecasting Analytics: AI can predict price fluctuations, identify irregularities, and offer insights into market behaviors, promoting proactive strategies and favorable investment results.
Client Analysis: AI technologies can evaluate customer habits and preferences, tailoring investment suggestions and enhancing client engagement and loyalty.
Regulatory Compliance: AI algorithms can automate compliance evaluations and optimize regulatory reporting, ensuring compliance with industry regulations while minimizing operational risks.
Text Analysis: NLP algorithms can analyze large volumes of text from news, social media, and financial documents to derive sentiment analysis and essential insights for investment strategies.
Fraud Prevention: AI can identify suspicious patterns or fraudulent activities in financial transactions, bolstering security measures and safeguarding investor assets.
AI Agents are an excellent way to boost efficiency and ensure compliance within business processes. AI Agents are specialized assistants, powered by Gen AI to perform certain tasks. Once added to a business process, they can operate without human intervention within the process flow, as they are fed information and perform actions.
Bizagi AI Agents perform tasks such as content creation (emails, documents etc), summarize information such as long meeting notes or research articles, obfuscate sensitive data, or analyze information by detecting sentiment, tone and logical groupings.
One example of using AI Agents in asset management is as part of managing ESG requirements.
It’s vital for investment teams to work with bond issuers to convey how they are meeting their ESG objectives. But it can be a complex process. ESG agents can assist ESG analysts by:
- Retrieving vital details from within lengthy ESG report documents, saving the analyst from reading through the whole document.
- Generating an overview of engagements and meeting information, for top-level reporting.
- Creating time frame reports on multiple meetings, and comparing notes across multiple engagement meetings to save time of reading, comparing and writing up documents.
As it stands lots of questions go unanswered. Understanding complex and unstructured data today means coming up with queries that go over to IT, who then need to manually analyze data and create dashboards to get the business insight that’s needed. Generative AI can help with this as well. Using natural language questions, analysts can simply interact with an AI tool like Bizagi’s Ada Assistant, and get answers in real-time, with data visualized in front of their eyes, and the ability to take actions on the data such as advancing or creating cases that require action.
It’s clear that generative AI combined with automation will play an important role in the future of asset management operations. Bizagi is working with several asset management organizations around the world to get value from AI technology in operations now. We expect to see improvements in overall efficiency, significant reductions in manual effort and better use of data to drive business decisions.
If you’d like to know more about Bizagi’s AI capabilities, look at our solutions and success stories for Investments and Asset Management, AI Agents, and Ada.
