Based on our analysis of deep learning applications in finance, we’ve identified 11 key use cases where AI-driven models are making an impact.
These examples are drawn from real-world implementations across financial institutions and cover areas such as fraud detection, risk assessment, and investment strategies.
Financial services executives prioritize operational efficiency (80% consider it very/rather important) and cost savings (73%) as the primary drivers for AI implementation, while strategic initiatives like market expansion (45%) and new business model development (38%) rank lower in importance according to PwC’s AI adoption study1 .
Top 11 Use Cases of Deep Learning in Finance
1. Customer Service
Financial services companies use finance-specific chatbots with deep learning models to improve user experience. Deep learning in finance solutions brings personalized services to customers. According to the customer’s financial activities, virtual assistants can
- automate frequently completed actions
- suggest products that are not used by the customers, but can be a good fit for them
- answer questions
Deep learning in finance algorithms can identify potential churn by analyzing interactions. This capability helps insurance/insurtech companies and banks to offer discounts and new plans and protect their customer base.
2. Financial Security and Compliance
Deep learning algorithms enhance financial security by detecting suspicious activities and automating compliance processes with greater accuracy than traditional rule-based systems.
Security Applications:
- Real-Time Fraud Detection: Identify suspicious transactions with high precision across millions of daily transactions
- Compliance Automation: Use satellite imagery and street view data to verify business existence for KYC processes
- Risk Monitoring: Analyze unstructured data sources for compliance violations
- Regulatory Reporting: Generate accurate audit reports and regulatory submissions
Operational Benefits:
- Cost Reduction: Automate manual compliance processes
- Improved Accuracy: Reduce false positives in fraud detection
- Regulatory Compliance: Ensure adherence to evolving regulations
- Enhanced Coverage: Monitor previously undetectable patterns
3. Insurance Underwriting
Insurance companies use historical consumer data to train deep learning models. These models leverage vast amounts of historical consumer data for more accurate risk assessment and pricing decisions.
Data Sources Analyzed:
- Health Records: Medical history and current health status
- Wearable Device Data: Activity levels, heart rate, sleep patterns
- Demographic Information: Age, income, profession, location
- Financial History: Credit scores, loan payments, asset ownership
- Lifestyle Factors: Social media activity, spending patterns
Underwriting Improvements:
- Decision Consistency: Eliminate human bias in risk assessment
- Risk Prediction: Identify potential claims with greater accuracy
- Premium Optimization: Set appropriate pricing based on individual risk profiles
- Process Acceleration: Reduce underwriting time from weeks to hours
4. Insurance Claims
Deep learning enables automated damage assessment and fraud detection in insurance claims through computer vision and document processing capabilities.
Automated Claims Assessment:
- Vehicle Damage Analysis: Process accident photos to estimate repair costs
- Property Damage Evaluation: Assess home insurance claims using satellite and drone imagery
- Medical Claims Review: Analyze medical documents and billing for accuracy
- Fraud Detection: Identify potentially fraudulent claims through pattern recognition
Technology Applications:
- Computer Vision: Analyze images and videos for damage assessment
- Natural Language Processing: Extract information from claim documents
- Pattern Recognition: Identify suspicious claim patterns and behaviors
- Predictive Modeling: Estimate claim costs and processing requirements
5. Lending
Deep learning in finance models uses learned patterns and document processing results to assess credit risks and loan requests.
This data covers income, occupation, age, financial assets, credit scores, overdrafts, outstanding balance, foreclosures, and loan payments. Then, they can decide on the client’s qualifications for lending.
6. Algorithmic Trading
By analyzing historical data & current price movements, and extracting information from the news simultaneously, deep learning algorithms can predict stock values more accurately.
These predictions are used for fast trading decisions. Due to a lack of emotions, predictions, and decisions, deep learning in finance models delivers are more neutral/objective and data-driven.
Deep learning algorithms analyze historical market data, real-time financial news, and macroeconomic trends to predict price movements. They are often used to:
- Automate trade executions based on predictive models.
- Optimize portfolio performance by identifying and capitalizing on profitable opportunities.
Real-World Implementation: Quantitative hedge funds utilize Long Short-Term Memory (LSTM) networks to make split-second trading decisions based on real-time data streams. These systems can process thousands of variables simultaneously and execute trades faster than human traders.
For more, feel free to read our comprehensive list of AI use cases in finance.
7. Customer Support via Chatbots
Deep learning in finance enables AI chatbots to handle customer service. Financial services companies deploy deep learning-powered chatbots and virtual assistants to enhance customer experience and operational efficiency. These systems go beyond simple rule-based responses to provide personalized, contextual interactions.
Capabilities:
- Automated Action Completion: Handle routine transactions like balance inquiries and fund transfers
- Personalized Product Recommendations: Suggest financial products based on customer profile and behavior
- Natural Language Understanding: Process complex customer queries in conversational language
- Churn Prediction: Identify customers at risk of leaving and trigger retention strategies
Real-World Implementation: Bank of America’s Erica chatbot utilizes deep learning to deliver personalized financial advice and resolve customer issues efficiently. The system handles millions of customer interactions monthly, providing 24/7 support while reducing operational costs.
8. Portfolio Optimization
To optimize portfolios dynamically, deep learning in finance models evaluates market conditions, risk tolerance, and investment goals. These models:
- Predict asset price movements using historical data and news sentiment.
- Suggest the best asset allocation strategies for maximum returns with minimized risks.
- Enhanced investment strategies tailored to individual needs.
- Real-time rebalancing of portfolios to capitalize on emerging opportunities.
Real-Life Example:
Multi-agent AI architectures are emerging as sophisticated solutions for wealth management, coordinating multiple AI agents to handle different aspects of financial advisory services. These systems typically integrate with existing CRM platforms and data sources to provide comprehensive client management.
Cognerium Agentic AI represents this approach with their Multi-Agent General Intelligence API designed for wealth management applications.
The multi-agent architecture allows different AI components to specialize in specific tasks while coordinating to provide holistic wealth management services. This approach can potentially improve portfolio management efficiency and client engagement through automated advisory processes.
Such platforms demonstrate how AI agent systems are being applied to coordinate complex financial workflows, from client onboarding to ongoing portfolio optimization and compliance monitoring.
9. Regulatory Compliance and Reporting
Deep learning in finance automates compliance monitoring by:
- Analyzing transactional data to detect regulatory violations.
- Generating accurate reports for audits and regulatory submissions.
- Reduces the risk of penalties by ensuring adherence to complex regulations.
- Automates repetitive tasks, saving time and operational costs.
Global banks deploy deep learning systems to identify and report suspicious activities under Anti-Money Laundering (AML) laws, improving regulatory compliance efficiency.
10. Credit Scoring and Risk Assessment
Deep learning models transform lending decisions by analyzing comprehensive data sets and identifying subtle patterns that traditional credit scoring methods miss.
Enhanced Data Analysis:
- Traditional Credit Data: Payment history, credit utilization, account age
- Alternative Data Sources: Social media activity, spending patterns, employment history
- Behavioral Patterns: Transaction timing, spending categories, financial habits
- External Factors: Economic conditions, industry trends, geographic risks
Lending Process Improvements:
- Risk Assessment: More accurate evaluation of borrower creditworthiness
- Automated Decisions: Real-time loan approvals for qualified applicants
- Personalized Terms: Customized interest rates and loan conditions
- Portfolio Optimization: Better balance between risk and profitability
Real-Life Example:
Zest AI leverages deep learning in finance to develop more inclusive and accurate credit scoring models, enabling lenders to serve a broader customer base.
11. Anti-Money Laundering (AML)
Anti-money laundering (AML) is a critical area of deep learning in finance, requiring sophisticated tools to identify and prevent illicit activities.
Deep learning has become indispensable in strengthening AML efforts by analyzing complex transaction data, identifying anomalies, and mapping intricate financial networks that traditional methods often miss.
- Detects suspicious transaction patterns in real-time: Deep learning models can instantly process vast amounts of transactional data, flagging unusual activities such as sudden spikes in transaction volume or irregular account activity across multiple jurisdictions.
- Identifies complex money laundering networks through graph analysis: Using advanced graph-based neural networks, deep learning tools uncover hidden connections between accounts, entities, and transactions. This capability is crucial for detecting multi-layered laundering schemes that involve numerous intermediaries.
- Flags high-risk accounts and transactions for further investigation: Algorithms prioritize flagged activities, categorizing them by risk level, and sending alerts to compliance teams. This ensures that financial institutions focus on the most critical threats.
- Supports regulatory compliance with automated reporting: Deep learning systems streamline AML compliance by generating comprehensive, accurate reports for regulators. These reports include details of suspicious activity, supporting documentation, and the rationale behind flagged transactions.
Real-Life Example:
Quantexa, a provider of AML solutions, leverages deep learning to analyze transaction data and map relationships between entities. Using contextual AI, Quantexa uncovers hidden money-laundering networks, helping financial institutions proactively mitigate risks and comply with global AML regulations like the Bank Secrecy Act and FATF standards.
Challenges
Banks are traditionally risk-averse institutions since they have suffered significantly in financial crises when risky bets led to bank failures. For example, in the area of lending, this has led to the boom of fintechs focused on lending, like SoFi.
In addition, banks and insurers are highly regulated institutions and need to be able to show that for example their lending or underwriting decisions do not exhibit bias.
Therefore, deep learning’s challenges (i.e. hard to explain predictions) pose unique challenges for banks.
If you are ready to use deep learning in your 66firm, we prepared a data-driven list of companies offering deep learning platforms.
For more on how technology is transforming financial services, learn about
FAQ
1. What is deep learning, and how is it used in finance?
Deep learning is a subset of artificial intelligence that uses neural networks to analyze and process large amounts of data. In finance, it is used for tasks like fraud detection, risk management, algorithmic trading, and customer personalization by identifying patterns and making highly accurate predictions.
2. Why is deep learning relevant in finance?
Finance deals with both structured and unstructured data such as documents and text. Deep learning allows financial firms
to convert unstructured data into structured, machine-readable data. For example, this allows banks to get financial information on companies from their annual reports published in regulatory platforms like the Companies House in the UK
to make predictions & classifications on structured data. Data such as stock market information is highly structured and can be used to automate trading activities
3. How does deep learning in finance improve fraud detection?
Deep learning models can analyze vast amounts of real-time transaction data, detecting anomalies and suspicious patterns. This helps financial institutions identify fraudulent activities quickly and reduce false positives, ensuring better security and customer trust.
4. What are the latest innovations in deep learning for finance?
Recent innovations include Transformer-based models for analyzing financial texts, federated learning for collaborative data analysis without compromising privacy, and advanced graph neural networks for detecting complex fraud or money laundering schemes.
5. How does deep learning help with compliance in finance?
Deep learning in finance automates compliance processes by generating accurate reports, monitoring suspicious transactions, and ensuring adherence to global regulations such as AML and KYC guidelines. This reduces manual workloads and ensures regulatory readiness.
If you need help in choosing among deep learning vendors who can help you get started, let us know:
This article was drafted by former AIMultiple industry analyst Ayşegül Takımoğlu.
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