AI Insights: How Regulators Worldwide Are Addressing the Adoption of AI in Financial Services Insights Skadden, Arps, Slate, Meagher & Flom LLP

Some of the companies that have heavily invested in security machine learning and are working extensively towards this shift include Adyen, Payoneer, Paypal, and Stripe. A robo-advisor is a personal financial management platform that has a background machine learning algorithm running unattended. The advisor trades on an investor’s behalf and manages their account using survey responses which human advisors usually run. Process automation is an interesting option for businesses looking to hire or outsource their financial processes, as well as for professionals who wish to streamline internal processes. AI in banking and finance has expanded to assess the creditworthiness of potential borrowers who do not have a credit history. The bank previously employed a team of lawyers and loan officers who used to spend 360,000 hours each year tackling mundane tasks and reviewing compliance agreements.

Kasisto is the creator of KAI, a conversational AI platform used to improve customer experiences in the finance industry. KAI helps banks reduce call center volume by providing customers with self-service options and solutions. Additionally, the AI-powered chatbots also give users calculated recommendations and help with other daily financial decisions. The report found 71% of customers want their financial services provider to have a clear digital process for opening an account. The majority of banking customers want to apply for credit and debit cards, and to open accounts online. Similarly, insurance customers said they prefer to buy, renew, and change coverage or file claims digitally.

Process Automation

It aids financial organizations in identifying pricing optimization options, prospective investment opportunities, and demand forecasting. Predictive analytics, for instance, is used by hedge funds and asset managers to estimate stock price changes and guide investment decisions. Personalized financial services are those that adjust financial goods, suggestions, and assistance to each individual customer’s unique needs and preferences using AI algorithms and data analysis. Personalized financial services include evaluating consumer data, including spending patterns, earnings, and investment aspirations, to offer individualized financial solutions.

Financial automation will undoubtedly affect the responsibilities of many staff members, so managers may have to re-engineer processes and redeploy resources to maximize productivity and output in more sophisticated and strategic areas. For example, when bank employees give biased advice based on AI recommendations, the entire institution may start systematizing bias into the decision-making process. AI might eventually be able to completely replace current mathematical credit scoring systems that get a lot of flak for being outdated—primarily because of their standardization and lack of sensitivity to individual disparities and nuances. AI may also assist lenders in identifying less visible risk characteristics, such as whether a borrower exploits their available credit. AI finds application in enabling better credit systems by developing a system where lenders can more correctly determine a borrower’s risk with the aid of AI regardless of the social-demographic conditions.

Financial AI, Why You Need It

We are already seeing several areas in banking services that have been taking advantage of this disruptive technology. The following are some use cases where AI has been most impactful within the BFSI industry. AI is an area of computer science that emphasises on the creation of intelligent machines that work and perform tasks like humans. These machines are able to teach themselves, organise and interpret information to make predictions based on this information. It has therefore become an essential part of technology in the Banking, Financial Services and Insurance (BFSI) Industry, and is changing the way products and services are offered.

By analyzing extensive customer information, such as transaction history, spending patterns, and financial objectives, generative AI algorithms can generate bespoke recommendations tailored to each customer’s individual circumstances. Additionally, Kim et al. utilized CTAB-GAN, a conditional GAN-based tabular data generator, to generate synthetic data for credit card transactions, outperforming previous approaches. Saqlain et al. employed a Generative Adversarial Fusion Network (IGAFN) to detect fraud in imbalanced credit card transactions. IGAFN integrated heterogeneous credit data, addressing the data imbalance issue and outperforming other methods in credit scoring.

Read more about Secure AI for Finance Organizations here.