The potential of artificial intelligence in finance in 2022 is immense, ranging from chatbot assistants to task automation and fraud detection. Moreover, it is a disruptive force that will create new winners and losers.
The financial services firms invested in artificial intelligence (AI) will be rewarded. According to McKinsey, AI leaders can expect to see a doubling of their cash flow due to their investments in AI.
7 Applications of AI In Financial Services
AI promises to help financial firms create better products, price risk, and products accurately and offer safer, more convenient financial services from personal banking to trading.
1. Personalized Banking
Since Capital One launched ENO in 2017, AI has come a long way in finance.
The most powerful function of artificial intelligence is its ability to unearth insights about a user and learn from those insights to develop more personalized solutions. The more data AI has, the better it is to personalize solutions. This is true of personal banking.
The financial services industry has already seen the emergence of chatbots that use natural language processing to offer round-the-clock financial guidance. In addition, wealth management solutions can now be personalized to a person’s financial standing, goals, risk profile, and other features.
2. Algorithmic Trading
Algorithmic trading executes trades according to preset parameters, allowing traders to slice and dice significant transactions, spread them out across many exchanges, and execute them at a fraction of a second, or over time, all to minimize price-slippage.
Algorithmic trading brings liquidity into the markets and lowers spread and commissions. Importantly, it takes the human element from decision-making, making trades more rational.
The gains of algorithmic trading are real: the most significant investor in history is not Warren Buffett or George Soros. Instead, it’s James Simons of Renaissance Technologies, the best algorithmic trader.
3. Credit Scoring
One study found that 77% of consumers prefer to use a debit or credit card to purchase.
AI has democratized access to credit while also allowing financial services firms to price their risk more accurately. This is because it helps financial services firms optimize their underwriting decisions by using several factors that help them more accurately assess historically underserved borrowers.
AI can replace traditional credit risk models. Where banks believe those old models still have life, machine learning models can be used to optimize parameters and improve the selection process according to existing regulatory frameworks.
4. Risk Management
Risk management is one of the most critical areas where AI is used in financial services.
Many financial services firms now use machine learning in the loan underwriting process and for the reduction of financial risk.
Natural language processing and text mining are essential tools in monitoring trader activity pursuing rogue and insider trading and market manipulation.
Financial institutions can analyze email traffic, calendar data, check-in and check-out times, and call logs, alongside trading portfolio data, to predict the odds of trader misconduct. This saves financial services firms a fortune in reputational damage and market risk.
5. Cybersecurity & Fraud Detection
Cybersecurity and fraud detection are the two most important arenas for AI after risk management.
An estimated 95% of cloud breaches are a result of human error. AI can increase cybersecurity by discerning the average from abnormal data patterns and trends and informing financial firms about unusual activity or discrepancies.
Machine learning has been used for credit card portfolios for years. Credit card transactions give banks a wealth of data to process and train their unsupervised learning algorithms. As a result, these models are highly accurate in predicting credit card fraud.
6. Automate Processes
According to Fintech News, bankers are focusing their push for greater automation on risk management (for trading and investment decisions), cybersecurity and fraud detection, compliance, and lending.
Financial advisors use AI, investors, and traders to automatically manage their trading risk by analyzing historical data and automating responses.
Cybersecurity & fraud are essential areas for automation because they already have existing infrastructure, budgets, and IT teams that require optimization.
Compliance is an area financial institutions typically see as a burden that adds nothing to profitability and are eager to automate compliance as much as they can.
7. Data Quality
Artificial intelligence can improve data quality to drive better decision-making by leaders.
Few firms employ a practical data quality framework to assess their structural data quality problems and report on them. Artificial intelligence can give firms a higher level of insight, alerts, analysis, and reporting.
According to one survey, 94% of IT professionals lack confidence in the ability of consultants, employees, and partners to secure customer data. AI is being used to reduce the number of false positives and human error rates.
Investments in artificial intelligence in finance are beginning to see a payoff. Personalized banking, credit scoring, risk management, cybersecurity and fraud detection, automation of processes, and enhancing data quality are some of the most significant use cases for AI in the financial services industry.
Financial firms have taken advantage of AI’s ability to speed up processes, take the human element out of decision making, automate processes and unearth insights that no human being can.
This has allowed firms to reduce their risk profile, develop better products, minimize costs and increase cash flows.