Artificial Intelligence (AI) is the talk of the town. AI has seeped into daily lives such that one has to now walk the talk.
AI has various flavors; each has its application, including reading emotions!
But AI in finance stands apart.
Finance AI transforms the way people interact with money. AI streamlines and optimizes processes in the financial services industry, from credit decisions to quantitative trading and financial risk management.
Finance AI uses machine learning (ML) technology to mimic human intelligence and decision-making to enhance how financial institutions analyze, manage, invest, and protect money.
The man-machine interactions are bound to get strong enough to blur the line between humanity and technology. Yes, we call that ‘technological singularity’.
Why is AI required in financial services?
Financial processes, such as data entry, collection, verification, consolidation, and reporting, have depended heavily on manual effort. These manual activities make the finance function costly, time-consuming, and slower to adapt. However, a few financial processes are consistent and well-defined, making them ideal targets for automation with AI.
The advent of ERP systems was a saving grace. They allowed companies to centralize and standardize their financial functions. Early automation was rule-based–as a transaction occurred or input was entered, it could be subject to a series of rules for handling. While these systems automate financial processes, they require significant manual maintenance, are slow to update, and lack the agility of today’s AI-based automation. Unlike rule-based automation, AI can handle more complex scenarios, including the complete automation of mundane, manual processes.
Increased automation also means improved accuracy across financial processes. High-volume, mundane processes can lead to fatigue, burnout, and error in humans. Computers, however, do not have these same limitations. They can also process drastically higher volumes of transactions in a given period. The result is better data to work with and more time for the finance team to put that data to use.
What are the benefits of AI in financial services?
AI lets financial services companies 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 harvested personal data determines 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 take required action to resolve it.
For banking customers, AI and ML can improve the overall customer experience. The rise of online 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.
Applications: How AI can solve real challenges in financial services
AI has superhuman abilities to transform how financial services companies engage with customers. Since everyone needs financial services, how we manage money will see a diametric shift.
Some of the promising applications of finance AI are:
Speech recognition
Convert speech to text to gain insights from customer interactions, such as contact center sales calls, and drive better customer service experiences.
Sentiment analysis
Identify sentiments, such as the prevailing emotional opinion in a given text, in investment research or chat data, using natural language AI.
Anomaly detection
Detect anomalies, such as fraudulent transactions, financial crime, spoofing in trading, and cyber threats.
Recommendations
Deliver highly personalized recommendations for financial products and services, such as investment advice or banking offers, based on customer journeys, peer interactions, risk preferences, and financial goals.
Translation
Make content, such as financial news and apps, multilingual with fast, dynamic machine translation. It enhances customer interactions and reaches more audiences wherever they are.
Document processing
Extract structured and unstructured data from documents and analyze, search, and store this data for document-extensive processes, such as loan servicing and investment opportunity discovery.
Image recognition
Derive insights from images and videos to accelerate insurance claims processing or expedite customer onboarding with KYC-compliant identity document verification.
Conversations
Delight customers with human-like AI-powered contact center experiences, such as banking concierge or customer center, to lower costs and free up human agents’ time.
Data science and analytics
Access a complete suite of data management, analytics, and machine learning tools to generate insights and unlock value from data for business intelligence and decision-making.
Predictive modeling
Use data customer, risk, transaction, trading, or other data insights to predict specific future outcomes with a high degree of precision. These capabilities can be helpful in fraud detection, risk reduction, and customer future needs prediction.
Cybersecurity
Automate aspects of cybersecurity by continuously monitoring and analyzing network traffic to detect, prevent, and respond to cyberattacks and threats.
Generative AI
Build new AI-powered search and conversational experiences by creating, recommending, synthesizing, analyzing, and engaging naturally and responsibly.
What are the risks of not implementing AI in finance?
According to the “Money and Machines” report, 87% of business leaders believe that organizations that do not rethink finance processes will face risks, including:
- Falling behind competitors by 44%
- More stressed workers 36%
- Inaccurate reporting 36%
- Reduced employee productivity by 35%
Companies that delay incorporating AI also run the risk of becoming less attractive to the next generation of finance professionals. 83% of millennials and 79% of Generation Z respondents said they would trust a robot over their organization’s finance team. Millennial employees are nearly four times more likely than Baby Boomers to want to work for a company using AI to manage finance.
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