Artificial Intelligence (AI) is starting to become aprofessional capability. In recent years, AI technology has been used invarious areas of the financial industry, including financial productrecommendations, trading strategy analysis, financial fraud detection, loanunderwriting, asset portfolio optimization, and security surveillance. Byutilizing these technologies, the financial industry is able to increaseefficiency and accuracy, while enabling faster market response and complexfinancial market analysis. In this article, I will introduce why the financialindustry needs AI, the technologies and use cases, and their advantages andlimitations.
Financial AI, Why You Need It
Financial institutions havemuch to gain by adopting AI to improve revenue and reduce costs. McKinsey, aglobal consulting firm, estimates that AI could deliver up to $1 trillion in value to global banks annually. Accenture estimates that financial AI services will add $1 trillion in new value to banks worldwide by 2035. AI technology isaddressing many of the limitations of traditional banking. From the complexityof financial transactions, to the vast amounts of proprietary and third-partydata, to structural problems in financial services such as the rise offraudulent behavior, it's tackling a range of issues that are ultimatelycreating benefits for end customers.
Improve the customer experience
89% of companies that have adopted financial AI use it to improve customer experience. AI has the potential to transform finance by enabling companies to offer a wide range of personalized financial services at affordable prices.
For example, the customer experience of financial transactions can be greatly enhanced with a conversational AI chatbot instead of a financial professional who is only available for a limited time. By making it easier for people to understand financial products and industries, they can reduce the amount of CS that occurs when buying financial products. Financial institutions can leverage vast amounts of data to suggest personalized investment strategies, quickly detect fraudulent activity, and efficiently evaluate fraudulent claims.
Improve operational efficiency
AI has ushered in an era of automation for activities as diverse as identity verification, credit scoring, loan approvals, and portfolio optimization, as advances in AI have dramatically reduced manual effort and increased accuracy. Financial institutions can use the savings to better serve their customers.
Increase profitability and revenue
In one report, 72% of financial services companies surveyed said they were adopting AI to increase revenue. This is because with increased efficiency, financial institutions can reduce costs and increase profits. Banks can leverage AI to make better investment decisions. They can also increase stability through effective risk management.
Fraud detection features
Would you believe that in 2022, fraud losses were estimated to be around $8 billion? The actual total cost to banks is said to be much higher than that. Fraud impacts banks' bottom line and threatens to drive up consumer prices, causing direct and indirect cost increases. To combat this, AI detects trends and analyzes massive amounts of data. Outperforming traditional fraud prevention solutions, AI is constantly improving to prepare for any risks that may arise in the future.
Financial AI Applications, Use Case
This graph shows the results of a survey of
banks and insurance companies in the DACH region about the potential use of AI. Nearly 80% of the executives surveyed want to increase digital efficiency in their business processes, and 73% want to benefit from cost savings. More than half also say they are already using AI in new areas such as chatbots, automation, and predictive marketing.
1. Investment and loan underwriting
AI is a game changer for financial analysts and asset managers, completely transforming the scale at which information can be collected and analyzed. Automatically identifying, extracting, and analyzing relevant information from structured and unstructured data sources increases the amount and relevance of data that analysts and managers can incorporate into their processes, allowing them to work much more efficiently and effectively. With LLM, a large-scale language model fine-tuned for finance, you can quickly summarize research and other data sources to help build investment portfolios.
Of the financial firms using AI for investment research, 75% are using it for content summarization purposes, and 62% are using it for data pattern identification and trend detection. In other words, financial services firms are using data from financial statements or historical market data, third-party databases, social media content, news, and images to train their models. Using AI to analyze these disparate data sources can yield a number of insights that can help businesses gain an edge.
In addition to investing, AI can be used to assess the creditworthiness of borrowers. This allows financial institutions to make more informed lending decisions. In addition, AI that provides automated investment advice can analyze large amounts of data and identify investment opportunities, making it easier for more people to invest their money and achieve their financial goals.
2. Anomaly detection and risk management
Financial scams are becoming more sophisticated by the day. This makes it difficult for financial institutions to meet the requirements of anti-money laundering regulations. However, with the introduction of financial AI, this is changing.
In 2022, the total cost savings of AI-enabled financial fraud detection and prevention platforms was approximately $2.7 billion globally, and is expected to total more than $10.4 billion by 2027. AI-enabled anomaly detection enables financial institutions to identify fraudulent transactions and spot anomalies in large data sets.
And risk management is critical for financial institutions. AI can assess and quantify overall risk by analyzing the probability of exposure to a risk and its potential impact. It can capture and measure the different types of risks an institution faces, providing a comprehensive rationale for the potential causes associated with those risks. AI can help organizations manage risk more effectively because it can identify more complex patterns and produce more accurate predictions than humans can.
This is especially true for credit scoring, where machine learning can be used to make unbiased, fast, and accurate credit assessments. Other benefits of AI-powered credit scoring include reducing manual labor and increasing customer satisfaction with faster card issuance and loan application processing.
3. Automate customer-facing processes
The introduction of AI to the financial services industry has enabled it to meet the increasingly complex needs of its customers. The key has been the ability for companies to automate repetitive, manual tasks such as document digitization, data entry, and identity verification, which can reduce the number and cost of tedious tasks that need to be performed by humans, with the added benefit of minimizing human error.
Technologies like optical character recognition (OCR) make it possible to instantly digitize checks, receipts, invoices, and more, while AI-powered facial recognition makes it easy to determine a customer's identity and whether their face is recognizable, while also verifying that the information is valid, all at once.
NLP, powered by machine learning, can extract relevant information from documents and generate reports. Reports can then be automatically generated based on this data, streamlining processes for customers and regulators. As RPA plays an increasingly larger role in day-to-day operations, your skilled employees will be able to focus on more valuable tasks.
4. Personalized chatbots
Excellent customer service is increasingly valued as a key competency to keep customers engaged and satisfied. However, relying on live agents for immediate, round-the-clock support is impractical and costly. Advances in conversational AI, as demonstrated by ChatGPT, are changing the game.
GPT-4, or Anthropic's Claude, a so-called large language model (LLM), has become known for its conversational chatbots that understand customer intent and respond in a human-like manner. Building on this, many financial institutions have initiated projects to customize their models to provide the best response and align with policies. Tailoring models trained with proprietary data through techniques such as fine tuning, prompt tuning, and discovery has emerged as a key agenda. Based on individual customer profiles and preferences, these trained AIs are now able to provide personalized financial advice and guidance and assist users with budgeting, financial planning, and investment decisions.
5. Personalized portfolio analysis
Automated wealth management platforms can use AI to tailor portfolios to match each client's disposable income, risk tolerance, and financial goals. All an investor needs to do is complete an initial survey to provide this information and deposit a set amount each month. Robo-advisors work by selecting and purchasing assets as needed, and then readjusting their goals as needed to help clients meet their objectives. Their increasingly competent machine learning models allow them to analyze more data and provide more personalized investment plans.
Robo-advisors are most valid for people who are interested in investing but struggle to make investment decisions independently, as they are a much cheaper option than hiring a human wealth manager. They are becoming a popular choice, especially for first-time investors with a small capital base.
Historically, portfolios have been difficult to value manually because of the many factors that need to be considered, such as the type of investment. To address these challenges, many financial institutions are introducing AI into their portfolio valuation process. With automated and accurate AI-powered asset valuations, financial institutions have been able to improve their decision-making to make accurate and efficient decisions. Models utilize large amounts of financial data, such as historical market data, company financials, and economic indicators. Based on this, they help organizations identify patterns, correlations, and trends that affect portfolio valuations.
Techniques and examples of financial AI applications
In this section, we'll introduce the technology applied to AI in the financial industry.
- Transcribe STT
speech to text to improve service with insights from customer interactions and deliver a better customer service experience - Sentiment analysis
Identify emotional opinions and the sentiment of specific text using NLP, including investment research, chat data sentiment, etc. - Data processing
Extract structured and unstructured data and analyze, search, and store data for document-intensive processes. - Image recognition
Assess damage to property, such as real estate or vehicles, from images and videos, speed up insurance claims processing, or accelerate customer onboarding by verifying identity documents. - Chatbots
Reduce costs by delighting customers with human-like AI-powered contact center experiences, transforming personal finance by bringing smart, intuitive experiences to apps, websites, digital platforms, and virtual tools. - Machine Learning
Use data insights to predict specific future outcomes with high precision; learn large amounts of financial data to discover patterns and use them to build predictive models - Cybersecurity
Automate aspects of cybersecurity by detecting, preventing, and responding to cyberattacks and threats. - Generative AI
Auto Agent dramatically streamlines human-intensive financial data-related tasks, such as developing products after analyzing asset markets, automating claims payments, and monitoring trends.
Ultimately, AI enables data management, analytics, and leveraging machine learning and tools to gain insights and create value from data for business intelligence and decision-making.
👉 Learn more about OCR in finance and banking automation
Financial AI use cases
BloombergGPT
Bloomberg is playing an innovative role in the financial industry by utilizing AI in a variety of areas. Bloomberg has developed BloombergGPT, a fine-tuned version of GPT, to provide accurate answers to financial questions. The model has also been evaluated on sentiment analysis tasks and outperforms other models. (Link to paper)
BloombergGPT has the ability to perform sentiment analysis, news categorization, and other financial tasks. This enables us to quickly analyze financial market data and information to provide a variety of services, including financial product and investment recommendations and trade alerts. In particular, it provides financial analysis services utilizing artificial intelligence technology called Bloomberg Terminal to provide reliable market information and data to professionals and institutional investors.
Aicel
Together with DataHunt, Aicel is a Korean subsidiary of FiscalNote in the US that collects data from asset markets and processes it in real time to predict asset market prices and make accurate investment decisions.
Aicel is a DaaS data engine, and AiM processes and derives insights from a variety of data that influence the prices of asset markets. It collects various financial unstructured data such as real-time news, social media, corporate IR reports, product reviews, customs export data, and card company payment data, and when combined with structured data, accurately weights and matches assets with sophisticated algorithms and know-how. This enables us to deliver unrivaled value, including
- Investment professionals such as hedge funds & asset managers
Trade, credit card payments, e-commerce, news sentiment analysis, and other alternative data provided by Aicel can help you predict the value of an asset with real-time data before the company goes public. - General Business
We provide a variety of data services for business planning, sales marketing, and investor relations, including analyzing consumer reactions to your company's and competitors' new products, analyzing the competitive environment and market share in your market, and capturing changes in consumption trends to help you maximize the value of your assets.
In addition, we are providing financial data platform and big finance for B2C customers, and will soon release an AI agent service to help people invest in difficult assets through LLM.
KB Bank
KB introduced the 'BICS (Big data CSS)' based on the latest machine learning model to assist corporate loan officers in determining credit risk.
'BIcs' utilizes various information such as financial and non-financial information to analyze the credit risk of companies to be financed. It is also equipped with a function to predict which companies will grow into blue-chip companies in the future.
In addition to this, we provide an automated model training pipeline to retrain the model on a yearly basis, and reports on the results of 'Bics' runs are also automatically generated and provided to corporate credit officers.
In addition, KB is conducting a project to upgrade the 'AI Voice Phishing Monitoring System' to strengthen the prevention of voice phishing.A pipeline for retraining AI models by collecting training data and variables has also been established. These features will enable corporate credit officers to make easier and more accurate judgments on credit approval/rejection.
Limitations of Financial AI
- Data-dependent
AI requires large amounts of accurate data. However, in the financial sector, data quality and availability can be limited, which can limit the performance of AI models. - Difficulty interpreting causality While
AI models can identify patterns and correlations, it can be difficult to clearly understand causality. This requires caution in financial decisions where interpretation and explanation of predictions are important. - Dealing with uncertainty
Financial markets are a highly uncertain and volatile environment. AI may not be able to fully resolve these uncertainties, and its predictions may be wrong. - Ethical concerns With the
adoption of AI technologies, privacy and data protection issues have come to the forefront.
Financial institutions need to ensure that customer data is adequately protected and that appropriate guidelines and regulatory frameworks are in place for ethical use.
Lack of human interaction Financial services requires interaction with customers and personalized advice. But because AI doesn't fully understand human emotions, it's limited in its ability to handle complex interactions.
Conclusion: Using AI to transform financial services is essential, but continued research is needed to overcome limitations
In the financial industry, AI technology is essential for innovation and competitiveness. Traditional financial services that do not utilize AI are expected to become less competitive and struggle to attract customers. To remain competitive in the future market and respond to changes, the financial industry must actively adopt AI technologies and focus on developing its own technologies. This will drive growth and development in a variety of areas, including innovative service offerings and increased efficiency, fraud detection and prevention, improved customer service, and risk assessment and management.
However, data collection and processing know-how is critical to AI adoption to provide reliable information, and financial institutions must take strict measures for data protection and privacy management and comply with regulatory requirements to build reliable AI systems.
The financial industry is encouraged to adopt AI to respond to constantly evolving technology trends and meet changing customer needs. It is an essential strategy for staying competitive in the future market and building successful business models.
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