AI in Finance: 5 use cases and applications

The OECD has undertaken significant work in the area of digitalisation to understand and address the benefits, risks and potential policy responses for protecting and supporting financial consumers. The OECD has done this via its leading global policy work on financial education and financial consumer protection. Data privacy can be safeguarded through the use of ‘notification and consent’ practices, which may not necessarily be the norm in ML models. For example, when observed data is not provided by the customer (e.g. geolocation data or credit card transaction data) notification and consent protections are difficult to implement. The same holds when it comes to tracking of online activity with advanced modes of tracking, or to data sharing by third party providers. In addition, to the extent that consumers are not necessarily educated on how their data is handled and where it is being used, their data may be used without their understanding and well informed consent (US Treasury, 2018[32]).

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. The financial industry encompasses a number of subsectors, from banking to insurance to fintech, and it’s a highly competitive industry as banks and other operators are constantly looking for an edge on one another.

Customer Service

Predicting customer behavior has been a constant challenge in finance ai solutions; however, AI and ML now make this a more accessible objective. Furthermore, they offer personalized advice leveraging various AI/ML use cases thereby delivering tailor-made solutions at lightning speed. Additionally, AI optimizes big data analysis instrumental for gaining insights on client behavior and preferences contributing efficiently towards target marketing efforts. Luckily, we’re now able to safeguard digital platforms by utilizing machine learning algorithms which continually learn from previous fraudulent attempts made henceforth enhancing transaction safety.

ML-powered classification algorithms can easily label events as fraud versus non-fraud to stop fraudulent transactions in real-time. The financial sector has quickly adopted AI technologies to improve operational efficiency. AI can help identify and flag errors in financial documents, speed up the loan approval process and automate customer service tasks. Financial institutions also use AI to detect fraudulent activity and protect against money laundering. AI can help financial institutions innovate and create new products, services, and solutions for their clients.

3.4. Training, validation and testing of AI models to promote their robustness and resilience

This comprehensive approach safeguards the confidentiality and integrity of financial information. They provide valuable information and show you what’s happening in the AI-finance world. With AI, we can actually enhance problem solving, planning, and decision making in fields such as robotics, healthcare, and finance. There’s so much buzz around AI because of the incredible potential it has to forever change the way we approach complex problems and find innovative solutions.

Others are using natural language processing and speech recognition technologies to increase efficiency in the back office. Automated chatbots and voice assistants have become more efficient for customer interactions. Finance professionals can offer consumers a personalized customer experience by leveraging artificial intelligence to analyze data and make accurate predictions. Incorporating artificial intelligence into the finance industry has their employees to become more efficient and effective in their work.

Additionally, with the help of machine learning in banking, companies can remove gender, racial and other conscious or unconscious bias and serve a wider audience more equitably. As you can see, ML in credit scoring brings a whole range of benefits, with customers receiving loans in a few clicks without leaving their homes. Generative AI also empowers financial institutions to analyze large volumes of financial data, trading volumes, and market indicators. It provides valuable insights that can inform investment decisions, risk management strategies, and fraud detection methods.

As the responsibility of data curation shifts from third party nodes to independent, automated AI-powered systems that are more difficult to manipulate, the robustness of information recording and sharing could be strengthened. In a hypothetical scenario, the use of AI could further increase disintermediation by bringing AI inference directly on-chain, which would render Oracles redundant. In theory, it could act as a safeguard by testing the veracity of the data provided by the Oracles and prevent Oracle manipulation.

Oracle’s AI is directly interactive with user behavior, for example, showing a list of the most likely values that an end-user would pick. Prebuilt AI solutions enable you to streamline your implementation with a ready-to-go solution for more common business problems. Oracle’s AI is embedded in Oracle Cloud ERP and does not require any additional integration or set of tools; Oracle updates its application suite quarterly to support your changing needs.

• Now, as finance faces increased expectations to work efficiently and provide strategic insight, organizations must adopt AI technologies that offer greater automation, integrity, and accuracy.
• AI is not only giving HR professionals better ways to connect with employees but also assisting companies to hire better talent and retain them.
• If financial organizations can provide data privacy, customers will have less hesitation in allowing them access to their personal data.

AI has the ability to analyze and single-out irregularities in patterns that would otherwise go unnoticed by humans. The profile of artificial intelligence has risen massively recently, mostly as a result of ChatGPT, customer service chatbots, and generative AI. Likewise, credit decisions that previously required people to process vast amounts of customer data and credit history are now accurately informed by AI systems. Further, the use of NLP can aid text mining and analysis of social media data such as tweets, Instagram posts, and Facebook posts, which impact trading decisions. Yet another exciting facet is the use of reinforcement learning-based AI models, which can adjust to dynamically changing market conditions. Thus, AI/ML models enable traders to make more informed decisions, manage risk, and maximize profits.

WHAT IS AI IN FINANCE?

AI and blockchain are both used across nearly all industries — but they work especially well together. AI’s ability to rapidly and comprehensively read and correlate data combined with blockchain’s digital recording capabilities allows for more transparency and enhanced security in finance. AI models executed on a blockchain can be used to execute payments or stock trades, resolve disputes or organize large datasets. Darktrace’s AI, machine learning platform analyzes network data and creates probability-based calculations, detecting suspicious activity before it can cause damage for some of the world’s largest financial firms.

• This can not only help lenders and financial institutions to manage risk, but also enable your firm to offer more targeted and personalized lending options to your customers.
• Finally, companies are deploying AI-guided digital assistants that make it easier to find information and get work done, no matter where you are.
• Smart use of AI allows you to win over customers, get ahead of industry trends, and boost your performance.

By deploying AI-powered chatbots and virtual assistants, banks and financial institutions can handle a large volume of customer queries efficiently and in real time. These virtual assistants utilize natural language processing (NLP) capabilities to understand complex financial questions and provide accurate responses promptly. In the financial industry, AI technology is essential for innovation and competitiveness.

Similarly, AI-powered fraud detection systems can help financial institutions detect and prevent fraudulent activity in real-time, reducing losses and improving customer confidence. Global financial institutions often need to design models across the multiple market areas they serve. The data must be consistent across different languages, cultures, and demographics to properly customize the customer experience. For example, voice-activated programs are used to save time searching for customer information in a database or through piles of documents.

Read more about How Is AI Used In Finance Business? here.