Everywhere you turn these days, AI and machine learning are top of mind for firms looking to leverage technology to get ahead. While many industries are taking great strides to profit from the AI buzz by applying these technologies to consumer or retail spending, asset managers and capital markets firms are turning their attention to the insights and signals they can derive from their institutional relationships.
Accenture’s 2022 study of AI in asset management surveyed financial advisors in Canada and the U.S. to assess their AI technology readiness. It found that financial advisors think AI tools will help them with insight, information, and efficiency:
- 83% believe AI will have a direct, measurable, and consistent impact on the client-advisor relationship in the next 18 months
- 83% believe AI can achieve a level of sophisticated advice and planning that will ultimately leave them competing with an algorithm for clients in the next 18 months
- 55% believe to a great extent that AI will have either a transformative or revolutionary effect on the future of financial advice within the next three years.
Yet even with this interest in leaning into AI-powered tools, many firms have not taken steps to move forward with their AI strategies – five out of 10 advisors feel like their firms are challenged to act on their AI vision.
Here we share how asset management and capital markets firms can capitalize on the growing role that AI and machine learning (ML) are playing in decision-making, recommendations, risk management, and compliance so they can benefit from moving faster and, more importantly, with a higher degree of accuracy.
Market news and data fuel AI and ML predictions
Asset managers and capital markets firms heavily rely on published market news and market data from providers such as FactSet, Preqin, Equilar, Pitchbook, and Refinitiv for prospect and pipeline development. Historically, this research has required highly manual intervention and, as such, has been difficult to embed in day-to-day operations.
Now, top firms use the data signals these providers offer to fuel AI predictive capabilities or to create proactive signals for deal and investment teams. For example, a stock that was just rated by Moody’s or a bond downgraded by Refinitiv offers a potential trade for interested institutional or individual investors.
These market data providers bolster training sets for AI through structured and unstructured data to help make recommendations. Both qualitative and quantitative data can inform decisions, which is why market data providers organize their datasets into discrete types for use in signaling models, such as:
- News and commentary: Financial news, global and domestic news, market commentary, commodities research and forecasts
- Macroeconomic data: Country data, economic indicators and polls, industrial activity
- Market data and pricing: Benchmarks and earnings, equities, fixed income, foreign exchange, cryptocurrency data
- Reference data: Index constituents and weightings, industry classifications, security identifiers, fixed income terms and conditions
- Specialized data: Commodities fundamentals, pricing and indices, deals and transactions intelligence, mutual fund data
- Risk, regulation, and compliance data: Financial crime prevention, KYC and AML data, risk screening, regulatory compliance
- Company data: Broker research and content, business classifications, competitors, events and commentary, earnings calls and transcripts, valuation
The Salesforce platform is supported by these third-party providers that package up the relevant provider insights about your clients and surface them in AI-generated recommendations, such as unexpected changes in revenue, earnings, or personnel. Many wealth and asset management companies will take those market data signals and mash them up with their own proprietary insights to create a composite AI prediction.
For example, the team at Equilar has developed capabilities with AI and machine learning to read through the thousands of pages of a company’s earnings report. Their technology pulls key insights from the report and identifies likely good market signals, such as executives receiving stock earnouts that vest in six months. A wealth manager can then build Salesforce workflows around these types of Equilar insights to reach out to the executives to help them with their wealth planning.
AI and machine learning help with fraud detection and prevention
An Ernst & Young study found that over the past three years, some wealth and asset management firms have experienced as much as a 500% increase in annual fraud loss from year to year due to large-scale events. To help combat fraud, organizations are leveraging AI to automatically flag suspicious activity or conduct due diligence screening for Know Your Customer (KYC).
AI looks at a wealth and asset management firm’s underlying data set and is trained to detect and surface AI insights about due diligence processes. For example, suppose a company is trying to make an investment in another company. In that case, it must go through KYC exercises to understand the counterparties involved with the transaction, such as knowing about risks with relationships to regimes that they do not want to be affiliated with or potential conflicts of interest.
In addition to due diligence capabilities, AI and machine learning apps that plug into Salesforce are being used for the middle-office legal and contracting diligence processes that can manage the contracts process and identify non-standard terms that firms may inherit in a deal. These apps can sift through reams of legalese to detect non-standard clauses, pull those out, and catalog them with the CRM’s help to sort, filter and properly manage contracts accordingly.
Silverline stays on top of industry news about the latest development for AI and machine learning as it relates to capital markets and wealth and asset management, such as the new Einstein GPT Trust Layer that prevents customer data from being stored outside Salesforce.