Artificial intelligence (AI) supports financial services in several ways, from data analytics to customer engagement.
This article explores key applications of AI improving security, efficiency, and decision-making from Wall Street to the everyday person.
5 Key Applications of AI in the Finance Sector
- Personalizing services and offerings for customers,
- Generating new opportunities,
- Managing risk and fraud mitigation,
- Enabling transparency and regulatory compliance,
- and streamlining operations to reduce costs.
By applying advanced algorithms, AI extracts insights from vast datasets, enabling more accurate performance measurement, predictions, and forecasting.
It performs real-time calculations at speeds far exceeding human capability.
For customer service, AI systems can respond intelligently at scale, retrieving information and interacting conversationally.
Overall, AI brings two key capabilities to finance: understanding and learning.
First, it deepens understanding of markets, customers, and digital journeys by revealing patterns within data.
Secondly, it learns continuously from new data, refining its analytical models and conversational abilities over time.
With these dual strengths of understanding and learning, AI promises to transform financial services through more informed, personalized engagement between institutions and clients.
Given the above explanation, machine learning stands out as a subset of AI that is guaranteed to leverage both understanding and learning. More of it is covered below.
What is Machine Learning and How is it Applied in Finance?
Machine learning (ML) is a branch of artificial intelligence that enables computer systems to learn and improve on their own, without being explicitly programmed.
It works by training models on large datasets, allowing the system to recognize patterns and make predictions.
A common technique used in ML is neural networks, which are modeled after the neurons in the human brain.
Neural networks can process complex data and identify relationships between inputs and outputs.
Another important ML method is deep learning, which uses neural networks with many layers that can learn increasingly abstract representations of the data.
This allows deep learning models to take raw data like images or text as input and produce highly accurate predictions.
Financial institutions leverage these ML techniques to train algorithms that can analyze data to solve specific problems, like detecting fraud or predicting stock prices.
As more data is fed into the models, they continue to learn and improve their performance over time.
ML is a powerful set of techniques that allows computer systems to train themselves to perform tasks, discover insights, and make intelligent decisions based on data — without needing to be explicitly programmed for every scenario.
Applications of Artificial Intelligence in Finance
1. Access to Financial Planning
AI-powered chatbots and robo-advisors are providing personalized financial guidance and portfolio management to the masses.
They analyze spending habits and offer tips aligned to each user’s unique goals.
More advanced robo-advisors like Betterment and Wealthfront automate investments leveraging AI to optimize and rebalance portfolios personalized for each investor.
Machine learning evaluates risk preferences, time horizons, and income needs to construct suitable investments often outperforming human advisors.
By automating routine advising tasks and learning from vast market data, AI expands access to 24/7 financial expertise at low costs.
2. Faster Lending Decisions Powered by AI
Applying for loans traditionally involved endless paperwork and long wait times.
However AI-enabled lending is slashing approval times and overhead costs using predictive analytics.
By processing thousands of data points from credit reports, bank statements, tax returns, and more, algorithms can evaluate risk and creditworthiness faster than any loan officer.
Startups like Upstart grant quick access to personal loans using AI, while ZestFinance validates business loan applicants in just hours.
For lenders, AI drastically reduces staffing needs while increasing lending volumes and sales.
Quicker decisions also improve applicant experience and satisfaction.
3. AI-Driven Fraud Prevention
With stunning accuracy, AI algorithms flag fraudulent transactions that slip past conventional systems.
Pattern recognition capabilities detect signs of;
- Identity theft,
- Payment fraud,
- Insurance claims abuse, and more.
Issuers analyze data including;
- Types of products purchased,
- Shipping addresses,
- and web session information to uncover fraud.
Machine learning models distinguish legitimate requests from deception better than rigid rule-based systems.
Big financial firms are also winning with AI.
Mastercard claims AI has reduced false declines by 80% while increasing fraud alerts by 30%.
Billions of fraud attempts are now prevented annually thanks to AI.
4. Revolutionizing Cybersecurity Defenses
The increasing threat landscape has overrun legacy security tools that rely on signatures and rules.
AI offers hope through techniques like:
- Behavioral analysis — Here anomalies that may indicate breaches based on baseline network activity profiles are spotted in real-time.
- Deception technology — Machine learning detects attackers using deceptive network traps and honeypots.
- Threat intelligence — AI processes global data to identify emerging risks and block them proactively.
- User behavior analytics — By analyzing typical user patterns, suspicious logins or data access raises red flags.
AI both detects known techniques faster and importantly, also recognizes novel never-before-seen threats missed by other defenses.
This combination of scale and adaptability makes AI the future of cybersecurity.
5. Automating Banking Operations
Banks are deploying AI to optimize efficiency.
Bots handle customer service queries, allowing staff to focus on relationship-building.
Meanwhile, back-end AI solutions automate processes like:
- Loan origination — Algorithms verify applicant identity and documents, calculate terms, and send completed contracts. This slashes workloads by up to 70%.
- Compliance reviews — Supervised learning tools analyze agreements and flag clauses violating regulations for human review.
- Payment processing — Smart networks interpret payment details, format transactions, and detect problems in real time.
- Contract analysis — AI reviews business contracts, extracts key details, and classifies them to streamline management and analysis.
As a result, banks can reduce the headcount devoted to routine tasks and instead invest in more strategic roles unlocking value from AI systems.
AI is constantly moving finance into a seamless, personalized, and secure future.
Customers gain 24/7 access to planning, lending, and wealth management once exclusive to the elite.
Transactions happen in seconds versus days. And billions in fraud evaporates thanks to constantly learning defense mechanisms.
While AI will not fully replace advisors or fraud analysts, it will augment their capabilities to deliver improved speed, accuracy, and service.
Financial institutions that strategically adopt AI will gain a sustainable competitive advantage through 2024 and beyond.
For consumers, AI unlocks new levels of security, transparency, and simplicity.