AI in Finance: Examples, Benefits, and Future Impact

Remember standing in the bank line to deposit a check? You had to fill out a slip, provide ID, and wait a couple of business days. Now, all you need is online banking and a decent phone camera — that’s the power of artificial intelligence (AI).

This is just one example of how AI impacts daily life and transforms vital financial services. The modern world demands accuracy and efficiency, and integrating AI in finance tackles these challenges with ease.

Here are some ways AI is transforming finance.

6 benefits of using AI in finance

The financial sector affects various fields of business — a local bakery might need faster bookkeeping, and an investment planner could need help optimizing client portfolios. Here are some ways finance AI tools make things easier:

1. Enhanced risk management

Fraud costs the finance sector billions annually. Institutions need a swift approach to loss prevention. Financial AI tools, like machine learning algorithms, flag potentially fraudulent activity more quickly, which reduces the impact.

2. Cost reduction through automation

Manual banking tasks like statement generation drain resources. AI automation reduces labor costs, eliminates human error, and frees up time and resources for more efficient use elsewhere. 

3. Improved personalization of services

Banking customers don’t want to feel like they’re just a number. AI data collection allows finance companies to generate unique profiles and tailor individual solutions for personalized advice. This personal touch strengthens long-term client relationships.

4. Broader financial services access 

AI lowers the entry barrier into industries like commodities trading. Where specialized knowledge seemed exclusive, machine learning technologies are leveling the playing field. 

Open-source platforms, such as eToro, use AI for stock analysis and portfolio development. Predictive analytics neutralizes the intimidation factor by helping industry newcomers make more confident decisions and learn as they go.

5. Streamlined compliance

The finance industry undergoes frequent regulatory changes, which have created compliance headaches in the past. Now, AI systems can minimize the manpower needed to maintain compliance standards. Algorithms analyze large data sets to identify and resolve compliance issues in real time.

6. Transparency

Have you ever been turned down for a loan without a clear explanation? Ambiguity in acceptance and denial procedures leaves room for biases. Financial AI tools are phasing out antiquated financial models, instead training AI to analyze data objectively to keep procedures honest. 

7 examples of AI in finance

Using AI tools in the financial sector builds a safer, precision-driven industry. Here are seven key examples of how various areas of finance integrate AI into their everyday operations:

1. Enhancements to customer service

When you visit a bank or other financial institution’s website, chatbots can answer your questions in real time — no more waiting for human service. Natural language processing (NLP) makes chatbots incredibly sophisticated, providing fast, personalized interactions. 

Advancements in speech-to-text AI software also improve call-handling speed and increase customer satisfaction rates. For example, Otter is a premium speech-to-text software that leverages advanced AI technologies to transcribe meetings and other audio — like the financial advisor meetings you need to take notes on. 

2. Algorithmic protection against cyber attacks

Human eyes can overlook suspicious behavioral patterns and leave financial institutions vulnerable to threats. AI algorithms pick up the slack. 

AI tools also utilize historical data for predictive analytics to identify potential future vulnerabilities and suspicious behavior. Financial institutions use these data points to create new security measures to prevent an attack before it happens. 

3. High-speed financial trading 

High-frequency trading (HFT) uses powerful tools to analyze the financial market and trade stocks automatically. This is a fast-paced arena, and advanced machine learning techniques boost its efficiency. 

Combining speed and algorithmic strategy is the key to success, which is why traders leverage deep learning AI networks to parse market data quickly. These AI tools factor in risk, market forecasting, and pattern recognition with greater speed and accuracy than previous methods.

4. Smart lending

Historically, creditworthiness has been a moving target. Organizations use tons of factors to decide how creditworthy you are — including every account and loan you have.

Integrating machine learning models makes the credit scoring system more balanced, fair, and transparent. Data collection helps analyze your current spending patterns, payment timeliness, and online behaviors, and operators can train machine learning models to use collected data to predict your loan repayment likelihood and give you better offers.

5. Automated document processing

Processing important financial documents manually takes time and energy that most institutions don’t have. Machine learning and NLP improve document management while eliminating human error.

Invoice sorting is a good example. AI can do it automatically in a fraction of the time it takes to do by hand, allowing workers to quickly scan and organize documents according to their content. 

6. Thoughtful financial planning 

AI financial advisors are a cheaper alternative to traditional advisors. These AI platforms provide investment advice with algorithms that consider market trends and other economic indicators.

Robo-advisors offer potentially better returns, but they can’t replicate a real advisor’s connection with their clients. Viewing AI as all-or-nothing is counterproductive. Instead, human advisors should harness new automation and research capabilities to give clients the best of both worlds.

7. Image recognition to reduce risk

The banking industry primarily uses AI image recognition to mitigate risks associated with fraud. AI image software can verify the validity of checks, credit cards, and other sensitive documentation — without needing humans to double-check.

AI also helps verify customer identities, which is often necessary for organizations (like banks) that need to comply with regulatory bodies.

What’s the future of AI in finance?

The future looks bright for AI in finance. For example, generative AI (Gen AI) is reshaping the banking industry. Gen AI creates new data sets and solutions based on previously learned information, meaning banks can streamline tasks like risk assessment and targeted investment strategies. The result is faster, more efficient processes all around.

Just don’t expect AI to totally take over financial tasks. While incorporating AI is promising, the human element still plays a key role in people’s experiences, and sensitive data often requires hands-on oversight. As applications of AI in finance evolve, so must human monitoring. 

The key is to balance AI with human oversight. Integrated responsibly, financial AI improves quality across countless applications. 

Otter: Making your financial conversations more productive with AI

Otter is your AI-powered tool for staying organized and efficient in the fast-paced world of finance. With Otter, you can keep track of all of your financial meetings and discussions, whether you’re talking about budgets or investment strategies.

Otter’s meeting agent eliminates manual notetaking from the equation — providing automated notes and summaries from your meetings. It’s easy to collect crucial information from past conversations — and in real time — to make better strategic decisions.

Give Otter a try and make your day more productive.