AI in Finance

What is AI in Finance?

Artificial intelligence (AI) in finance is the use of technology like machine learning (ML) that mimics human intelligence and decision-making to enhance how financial institutions analyze, manage, invest, and protect money.

How is AI powering the future of financial services?

AI in finance is modernizing the entire industry by streamlining traditionally manual banking processes and unlocking deeper insights from generated data, helping dictate how and where investments are made. AI is also changing the customer experience by creating faster, contactless interactions that include real-time credit approvals and improved fraud protection and cybersecurity.

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How does AI impact the finance industry?

AI is a large driving force for how financial organizations conduct risk management, which includes security, regulatory compliance, fraud, anti-money laundering (AML), and know-your-customer (KYC) guidelines. With AI as part of their infrastructure, banks, investment firms, and insurance companies can use it to perform real-time calculations to forecast performance, detect anomalous spending behavior, or maintain compliance, among a plethora of other applications.

What are the benefits of AI in financial services?

For financial institutions, AI lets organizations accelerate and automate historically manual and time-consuming tasks like market research. AI can quickly analyze large volumes of data to identify trends and help forecast future performance, letting investors chart investment growth and evaluate potential risk. Evaluation can also apply to insurance, where personal data can be harvested and used to determine coverage and premiums. AI can also be used for cybersecurity purposes, specifically identifying fraudulent transactions. By closely monitoring purchase behavior and comparing it to historical data, AI can flag anomalous activity, automatically alert both institution and customer to verify the purchase or transfer in real time, and if needed, take action to resolve it.

For banking customers, AI and ML can improve the overall customer experience. The rise of online banking (i.e., contactless banking) minimizes the need for in-person interactions, but the shift to virtual can create more endpoint vulnerabilities (e.g., smartphones, desktops, and mobile devices). AI can automate many basic banking activities like payments, deposits, transfers, and customer service requests. AI can also handle application processes for credit cards and loans, including acceptance and rejection, providing near-instant responses.

What are the challenges of AI in finance?

While most institutions believe AI and ML can improve how they do business and give them a competitive edge—98% according to one Forrester survey—about 80% to 85% of ML projects don’t launch due to various logistical and management issues or “last mile” problems, suggesting institutions need help from IT and AI network professionals to finish AI projects.

Beyond logistics, financial organizations also face many security and compliance regulations because of the sensitive and personal information they use every day. Any AI solution must be capable of protecting that data and abide by industry- and region-specific guidelines—since finance is a global concern and covers a large swath of companies.

The sheer volume of data itself is a complex challenge. For any AI solution to work effectively, institutions must have all their data in orderly pipelines and silos, letting ML accurately predict and forecast markets according to specific business objectives.

HPE and AI in Finance

The HPE portfolio helps the financial industry excel in a global economy within a digital-native world with cost-effective solutions that support better experiences for organizations and customers alike.

HPE GreenLake for Financial Services is an edge-centric, data-driven, and cloud-enabled platform that enables the financial industry to advance business outcomes, achieve faster time to value, and decrease operational costs that can be redeployed for new data-driven innovations. Even in a rapidly evolving regulatory environment, HPE GreenLake can accelerate development by optimizing the technology stack with hybrid cloud, deriving insight from a massive growing data set and using that insight to return customized experiences at the edge. HPE GreenLake is even offered as a service (i.e., a consumption-based model), meaning institutions gain the flexibility and scalability of cloud with the security and cost benefits of on-prem infrastructure.