How Artificial Intelligence Is Used in Finance
InternationalBanker.com, Sébastien Meunier | Apr. 9, 2018
On InternationalBanker.com, Sébastien Meunier, director of Chappuis Halder & Co., outlines how artificial intelligence is being used in five main applications in finance:
- Investing – asset management: algorithms can be used to search for correlations between world events and their impacts on asset prices, or to learn from publicly available social-media streams to anticipate markets’ movements (e.g., Kensho, Dataminr).
- Credit scoring – underwriting: machine learning can help lenders make more accurate credit-underwriting decisions, or advanced computer vision can be used with geospatial and aerial imagery for insurance/property underwriting (e.g., ZestFinance, Cape Analytics).
- Regulatory compliance – fraud detection: different channels and types of data can be analyzed with advanced pattern-matching analytics to detect fraudulent activity (e.g., Digital Reasoning, Actimize). Today, a typical anti-money-laundering process will perform an automated scan of incoming and outgoing payments based on predefined rules (country of origin/destination, name of the customer, etc.). Current systems generate a lot of false positives that are reviewed one by one by middle-office operators and/or compliance officers. Machine learning can be used to identify users to add to the whitelist, identify patterns to be added to the rule engine and ultimately reduce the number of false positives, saving costs while increasing the quality of the screening process.
- Market research – reporting: intelligent agents can curate and semantically index the financial-markets research content, and automate the writing of reports, personalized websites, emails, articles and more with natural-language-generation software (e.g., AlphaSense, Narrative Science).
- Customer support – assistants: intelligent agents can analyze incoming messages, route cases, provide customer-services agents with accurate suggestions, or help optimize personal-finance management (e.g., DigitalGenius, Pefin).
While large institutions have traditionally dominated this space due to their scale and financial resources, that may be changing, according to Meunier:
…Before financial institutions could hire technology experts to support their growth; now we see the Googles and Amazons of the world starting to hire business experts (traders, underwriters, etc.) and compete directly against established actors!
Regulation, while being a burden on the operations of incumbents, is still protecting the industry from a quick disruption. But for how long? Can financial institutions put up with just buying young competitors and integrating their products into their own services?