Because human decision making often deviates from data-based rationality, it’s ideal to augment human intelligence with technology like artificial intelligence and machine learning, according to Linedata.
In the recent whitepaper “Harnessing Human + Machine Intelligence in Capital Markets”, Ashmita Gupta, Global Head of Business Intelligence & Analytics at Linedata, said that tools like AI and ML are only effective if internal decision-makers fully buy into using them.
“Investment firms can encourage greater adoption by enhancing the explainability of AI models,” she said.
She added that many firms have long been using statistical models for decision making.
Now, with the exponential growth in data, including cheap storage and low-cost computing, AI-based techniques have grown in popularity as a way to deliver actionable results for businesses, she said.
According to a recent Deloitte study, “financial institutions that use AI in the investment process grow AuM by 8% and raise productivity by 14%.
The whitepaper stated that investment managers are pursuing AI-driven analytics to supplement humans in many different areas of their business, such as predictive analytics for trade operations, customer acquisition, and addressing operational risks such as fraud and regulatory concerns.
“With the power to augment monitoring and decision-making, Predictive Analytics based on AI can be a game-changer for compliance and risk management practice,” Gupta said.
However, what is unclear in many firms is how to best integrate and align AI with human decision-making to achieve the best outcomes, she added.
“Despite actively investing in AI and ML programs, organizations don’t have buy-in from key stakeholders across all levels,” Gupta argued.
According to the whitepaper, when key stakeholders and staff don’t believe AI and ML models can provide insights to make them better at their jobs, they’re going to revert to making decisions the same way they always have, using heuristics – mental shortcuts that allow people to make judgments quickly and efficiently.
“As humans, we’re not aware of our biases, but we often let them sway our decisions away from the insights AI and ML data models produce. Cognitive bias can cloud your choices and produce unfavorable outcomes,” Gupta argued.
She said that cognitive biases affect nearly every decision and certain biases tend to have the greatest impact in institutional investment settings.
In addition to cognitive biases, other factors such as a lack of internal alignment can contribute to poor AI adoption and suboptimal decision-making, according to the whitepaper.
Gupta argued that there are a number of ways to boost AI explainability, including involving users in model design and implementing ‘what-if’ tools for analysis.
“When investment firm users embrace the combination of human and machine intelligence, or hybrid intelligence, the business benefits include enhanced risk management and more profitable investment decision-making,” she said.
