10 ways in which AI will make finance better

Financial companies rely on streams of data and complex IT infrastructures, and operate in a constantly evolving regulatory environment, making them prime candidates for AI data-driven innovation. However, risks associated will need to be managed. Here are ten ideas as to where these developments will make a difference:

1. Hyper-personalized marketing and customer experiences:By leveraging alternative data, machine learning (ML) can be used to make predictions about customer behaviour and increase conversion rates. Messaging may become customized at an individual level based on personality profile, attitudes/beliefs, political preferences, purchasing behaviour, and preferred communication style, in a way that is indistinguishable from human written content.
2. More efficient and compliant collections practices:Natural language processing (NLP) models will enable more effective communication with delinquent borrowers, reduce human error, and lead to fewer CFPB complaints/lawsuits. AI will analyse customer interactions, flag conversations for follow-up, and turn all agents into compliant top performers.
3. Enhanced underwriting, originations, and risk management:ML and neural networks will lead to lower loss rates, and predictive models will enable real-time risk scoring and proactive identification of stressed credits. NLP will be used to draft legal documents and accelerate origination processes, allowing lenders/investors to deploy capital more quickly/effectively.
4. Highly-personalized, low-cost financial advice:AI will operate with duty-of-care to provide client insights, recommend investments, execute transactions, and generate financial plans based on individual circumstances/preferences, taking into account large volumes of data with the goal of delivering financial health.
5. Accelerated adoption of automated B2B payments. AI tools can help provide a faster B2B payment process through improved payment reconciliation by automatically matching payments to outstanding invoices and decreasing manual processes.
6. Infrastructure modernization and strengthening:AI coding capabilities will accelerate banks' digital transformation, including optimizing data centres and migrating applications to public and private clouds, helping to generate significant cost savings. Non-coders will be able to accomplish complex software projects using everyday language.
7. Fraud defence:Adversarial learning, a type of Generative AI that involves training two models against each other, will be used to improve fraud detection and take corrective action.
8. Regulatory reporting (and other management efficiencies):AI will enable non-specialists to generate custom reports (i.e. management and regulatory) while taking into account considerations that until now had been the responsibility of those with domain expertise. AI will also nearly eliminate time spent summarizing market research, conducting data analysis/pattern recognition, and other manual processes.
9. Improved data privacy:Generative AI can be employed to produce synthetic datasets that closely resemble original datasets, while adhering to privacy regulations. Instead of using client data that cannot be shared due to data protection laws/regulations, shareable data can be created using synthetic data.
10. AI risks will need to be managed, resulting in slower-than-expected adoption:Financials will need to manage AI coding errors, vulnerability to cyber attacks, biased models, unclear legal responsibility for AI decisions, and lack of AI traceability, among other risks.

FYI: these ideas came from Jefferies Equity Research. More info here.