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AI in Finance: 10 key examples

Alice Orecchio

AI in Finance comes with benefits in every application.

Artificial Intelligence is changing the way people interact with money. 

From quantitative trading to fraud detection, AI applied to Fintech is implementing and optimizing every process in the industry.

The best part is that everybody can benefit from this technological revolution: not only Financial institutions, including the industry’s stakeholders and workers, but also bank customers and investors.

As a matter of fact, AI enables 24/7 customer interactions, relieving the personnel from repetitive work, reducing false positives and human error.

This means a huge saving of time and money, for everyone.

Besides the pandemic, which accelerated the digital transformation from traditional banking to online and mobile banking which was already underway, the rise of Artificial Intelligence has proven to be a great opportunity to meet new standards for more personalized and digitized financial services.

In short, AI applied to Finance and Banking is providing customers with smoother, cheaper and safer ways to manage, save and invest their money.

Here are 10 key examples of how Artificial Intelligence is definitely a game changer in Finance and Banking:

1. Online and Mobile Banking

Along with Millenials, digital natives such as Gen Z customers have higher digital standards than the older generations, and they are considered one of banks’ largest addressable consumer groups. They definitely prefer digital channels when it comes to their bank, and the majority of them would rather never go to a physical branch.

Artificial Intelligence applied to online and mobile banking is a value added for all customers, perfecting tools to help them monitor their budget and make real-time spending adjustments.

2. Chatbots for Personal Finance

Consumers are willing to become more and more independent when it comes to their finances, and letting them manage their own financial health is a very good reason to adopt AI in personal finance.

AI assistants, such as chatbots, use Artificial Intelligence and natural language processing to provide self-help customer service, 24/7.

3. Insights for Wealth Management

Managing huge amounts of data, Artificial Intelligence can generate tailor made financial advice, giving personalized insights for wealth management.

These solutions dedicated to private investors help them make smarter decisions about their investments and take advantage of fast-moving markets.

4. Prevention of Fraud and Cyber Attacks

Artificial Intelligence can efficiently analyze patterns and extrapolates irregularities that would go unnoticed by the human eye.

This ability applied to Finance is vital to prevent fraud – such as money laundering  – and cyberattacks. Obviously, consumers want their banks and financial institutions to be reliable, and most of all they want secure accounts, in order to avoid online payment fraud losses.

5. Corporate Finance

AI technologies can help Corporates greatly reduce financial risk.  Machine learning models are particularly helpful in corporate finance as they can improve loan underwriting.

As seen before, AI can perform advanced fraud detection and doing so it can drastically challenge financial crime and spot anomalous activity.

6. Credit Decisions

By utilizing a variety of tools to accurately assess every type of borrower, AI solutions support banks and other credit lenders in the credit decision making process.

Credit availability is key for consumers, not only because it provides easier payment alternatives, such as debit or credit cards.

People’s most important life choices often depend on credit history, since having good credit means receiving better financing options, or even renting the house you want to live in. Therefore, a quicker and more effective approval process for loans and cards is a necessity.

7. Risk Management

Financial markets are relying more and more on Artificial Intelligence and machine learning to create safer and more agile models for risk management.

Every business needs more and more accurate forecasts to ensure fast and solid growth: AI algorithms can identify patterns of past behavior which led to incidents and treat them as risk predictors.

These predictions help financial experts to identify risks and ensure better information for future planning.

8. Thematic Investing

Thematic Investing is a top-down investment approach to diversify a portfolio, identifying macro themes that are more likely to achieve a long-term value increase.

Through alternative data and a proprietary analysis methodology, FinScience selects the most value-added themes and the stocks associated with each theme, monitoring the correlation between companies and topics through alternative daily metrics.

9. Quantitative Trading

Quantitative Trading is based on quantitative analysis, which relies on mathematical computations to identify trading opportunities.

Artificial Intelligence is certainly able to process large, complex data sets faster than humans, and this ability applied to trading highlights patterns for more strategic trades.

The resulting automation due to algorithmic trading processes saves valuable time while improving the outcome.

10. ESG Scoring

The ESG Score can apply to a company, a share or a fund, with a concise judgment on the three sustainability criteria: Environmental, Social and Governance.

ESG Scoring is meant to complete the traditional rating, providing more transversal and global information, thus improving the investment choices.

Artificial Intelligence ensures a complete assessment of companies, through the analysis of great amounts of external Alternative Data and combining them with the ones generated by company stakeholders, to provide a global perspective of corporate ESG performance.