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AI for Finance: 3 Ways It Revolutionizes the Industry

AI for finance can help reduce costs, improve investment returns, and increase profitability. By leveraging powerful machine learning algorithms, businesses gain access to a wealth of data-driven insights that enable more efficient decision-making.

AI for finance can help reduce costs, improve investment returns, and increase profitability. By leveraging powerful machine learning algorithms, businesses gain access to a wealth of data-driven insights that enable more efficient decision-making.

Artificial intelligence (AI) is not one technology but a group of technologies that include natural language processing, deep learning, and speech recognition, for example. These technologies may be used individually or in combination to deliver various solutions. According to McKinsey, AI technologies can help the financial services industry increase revenue, lower costs, and capitalize on unrealized opportunities. As AI advances, financial firms that fail to make AI part of their core strategy risk losing their competitive advantage.

What are some ways that AI technologies can help organizations maintain their competitive advantage? What areas can AI have the most impact? Three areas where AI for finance can improve operations are:

  • Compliance
  • Risk Assessment
  • Customer Service

Whether AI-powered searches or real-time fraud detection, AI technologies can transform the financial services industry.


AI for Finance: 3 Ways It Revolutionizes the Industry

Compliance requirements within the financial services sector continue to grow in scope and complexity. Anti-money laundering requirements, economic sanctions, and privacy laws demand comprehensive methods for evaluating compliance across the enterprise. Failures can result in costly penalties.

Privacy Laws

The most strict privacy laws went into effect in May 2018. The European Union’s General Data Protection Regulations include two regulations regarding personal information stored on a system that can impact financial firms.

  • Right to be forgotten. EU residents can ask that personal information be removed from a website.
  • Extraterritoriality protection. GDPR rules are not restricted to EU countries. They apply to any organization that collects data about EU residents, regardless of their physical location.

With AI technologies, organizations can perform searches to identify personal information. The searches would be faster and more accurate than having employees query multiple systems for information. Failure to comply can result in penalties of up to 2% of a company’s global revenue.

The United States does not have a federal privacy law, but individual states are addressing digital privacy. California Consumer Privacy Act (CCPA) implements rules similar to GDPR. As more states enact laws, financial institutions will need a solution to help them comply.

Know Your Customer

Financial institutions, fintech, and wealth management firms must comply with the Know Your Customer (KYC) guidelines. These guidelines are designed to prevent funding of terrorist activities, money laundering, and fraud. Each organization can develop its programs, but it must meet reporting requirements.

AI can help bring data together for reporting purposes, but it can also analyze multiple data points, including external sources such as social media sites, for more comprehensive evaluations. Whether confirming information or checking social media data, AI can ensure that the KYC guidelines are met.

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Investment companies have extensive reporting requirements. They must document that clients agree to investment activities. They also have obligations to report activities that may indicate tax evasion attempts or possible insider trading. Ensuring that all paperwork is in place and reports filed can be a time-consuming process.

For example, the Common Reporting Standard (CRS) attempts to minimize possible tax evasion. The standard requires participating jurisdictions to report non-resident account holders to the local tax authorities. Depending on a firm’s size, that requirement can take days if employees must search multiple systems. Not only can AI expedite the reporting requirements, but it can also analyze the data for patterns that only appear when massive amounts of data are used.

When the Base Erosion and Profit Shifting (BEPS) project publishes its findings, countries will probably add accountability requirements to the growing number of investment compliance regulations. Governments want to limit the tax avoidance activities of large multinational corporations.

Customer Service

AI for Finance: 3 Ways It Revolutionizes the Industry

AI technologies can do more than streamline compliance efforts; they can improve customer relationships through more personalized services. As more consumers move to digital services, AI can provide insights to help direct client-relationship management.

Client Engagement

Whether it is Netflix or Amazon, consumers expect that personal touch. They also expect a seamless experience no matter the channel. Disruptive financial services providers meet these expectations by processing transactions and approving loans in minutes, minimizing paperwork, and expediting services.

With one in three households having an intelligent speaker, financial institutions should consider smart speakers as another delivery channel. Customers could check balances, transfer funds, and pay bills while cooking dinner. Less time is spent on routine financial tasks, leaving clients free to enjoy other activities. AI technologies can facilitate this client interaction with its neural network, deep learning, and natural language processing.

Digital Services

In 2020, digital-only consumers between 30% to 41% making it the fastest-growing segment within the financial services sector. AI helps organizations deliver better digital services by analysing historical and current activity. Combining that information with external factors such as weather, social climate, and location, AI can predict future actions.

With AI, financial institutions can make strategic recommendations to their customers for financial products or services. For example, AI may identify a pattern where consumers transfer money among accounts. Knowing the pattern, AI might recommend a different type of account to maximize the customer’s return on investment. It may even suggest ways to automate the process so the transfers occur automatically.

Helping the customer before they need it makes for a better experience. According to McKinsey, this level of personalization translates into a 5% to 15% rise in revenue.

Risk Assessments

Whether approving loans or issuing insurance policies, financial services must evaluate risk. What is the probability that a loan will be repaid? What are the risks associated with insuring a company? Assessing risk accurately is crucial to long-term viability in the financial services sector.

Insurance Underwriting

Insurance companies use AI algorithms to assess risk. The technology uses historical data and external information such as age, income, and profession to predict and mitigate risks. The workflows can produce more accurate results more efficiently.

AI algorithms can go beyond a yes or no evaluation. They can look deeper into the data to identify extenuating or clarifying information. The technology can also discover risk factors that are not apparent in a rule-based implementation. Insurers then have the option of offering a more customized product that mitigates risk and increases revenue.

Fraud Detection

In 2020, large digital firms experienced an increase of almost 40% in successful fraud attacks, and lending platforms saw a rise of 28%. To reduce fraud, organizations need solutions that can mitigate risk in real-time. With digital services, the transaction can complete before any assessment.

Traditional fraud detection systems relied on rules. Sometimes, those rules resulted in false positives where valid transactions were flagged as fraudulent and declined. These situations embarrass and frustrate customers trying to complete transactions online or in person. No one likes to have a transaction declined at check-out.

Artificial Intelligence

AI is in its infancy when it comes to the financial services industry. Most deployments have been designed to streamline back-office processes and mitigate risk, but the technologies can identify new market opportunities and personalize customer interactions. F33.ai builds effective AI solutions to address the expanding needs of the financial services sector. Whether it is improved risk assessments or disruptive client engagement, F33.ai has the expertise to guide a project to successful completion.

Behind F33
Greg Bigos, CEO

Greg Bigos is the CEO of F33, bringing over decades of experience in delivering ERP solutions for manufacturing, logistics, and retail industries.

Contact Greg
Behind F33
Wit Jakuczun, CTO

Wit Jakuczun is the CTO/Chief Data Science Officer at F33 with a PhD in Applied Mathematics and over 18 years of experience in mathematical modeling, data analysis, and simulations.

Contact Wit

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