Artificial intelligence (AI) has the potential to completely transform the finance industry. According to Insider Intelligence, 75% of banks with over $100 billion in assets are already implementing AI technologies. McKinsey estimates that banking and financial services companies can create over $250 billion in value by applying AI.
While AI adoption is steadily increasing, there are still challenges like avoiding historical bias in credit scoring models that need thoughtful consideration. However, with a focus on responsible implementation, AI can reduce costs, accelerate processes, improve decision making, and enhance the customer experience across finance. Understanding key processes and using techniques like process mining helps ensure AI deployments align with overarching business goals.
This comprehensive guide provides an in-depth look at major AI use cases and examples across finance:
Introduction to AI in Finance
Let‘s start by looking at a few statistics that highlight the accelerating adoption of AI across financial services:
- 75% of banks with over $100 billion in assets are implementing AI technologies 
- 50% of financial services firms are projected to adopt AI by 2020 
- AI can generate over $250 billion in value for banking 
- top AI use cases include fraud detection, credit scoring, algorithmic trading, and robo-advisors 
Some key factors driving AI adoption in finance include:
- massive amounts of structured and unstructured data that can be used to train AI models
- potential to automate manual, repetitive tasks like loan processing and claims management
- need for real-time insights and personalization in areas like investments and customer service
- increasing model complexity that is difficult to manage without AI
- opportunities to improve efficiency, reduce costs, mitigate risks, and increase revenue
While adoption is increasing rapidly, thoughtfully evaluating processes, being mindful of bias, and taking an iterative approach are critical for successfully implementing AI in finance.
Now let‘s look at some of the top AI application areas and use cases transforming finance:
Lending is one area seeing significant transformation from the application of AI. Some examples include:
Retail Lending Operations
- Document processing: AI-powered optical character recognition and natural language processing extract relevant data from credit applications and supporting documents like bank statements and tax documents with up to 90% accuracy . This automates the applicant evaluation process with far less human review needed.
- Examples: Upstart uses over 1,600 data points and AI to provide faster, more accurate credit decisions . Their auto lending model approves 27% more borrowers than traditional models .
Commercial Lending Operations
- Risk assessment: AI analyzes financial documents, news, market data, and economic indicators to rapidly evaluate the creditworthiness of business loan applicants. This enables faster credit decisioning with lower default risk.
- Pricing: AI considers risk assessment, market conditions, and competitive factors to determine optimal pricing and loan structures for commercial clients. This balances risk and return.
- Portfolio monitoring: Ongoing monitoring of loans using AI helps identify accounts at risk of delinquency early. Proactive outreach can then minimize defaults. JPMorgan saw a 5% decrease in credit losses using this approach .
Retail Credit Scoring
- Predictive analytics: AI credit scoring models evaluate applicants using thousands of attributes to generate a near-instant credit decision. According to ZestFinance , this approach improved approvals by 10x while cutting default risk in half.
- Expanded access: Alternative data sources like rent payments enable AI models to score thin-file applicants with limited credit history. This expands access to credit.
- Fair lending: With thoughtful design, AI models can be tested for bias and monitored to ensure credit decisions don‘t inadvertently discriminate. This promotes fairness and compliance.
Commercial Credit Scoring
- Financial statement analysis: AI extracts insights 10x faster than human analysis of financial documents according to Automation Anywhere . This powers faster commercial credit decisions.
- Cash flow analysis: Understanding a business‘s cash flow is critical in credit evaluation. AI can rapidly analyze complex cash flow projections across multiple scenarios to avoid defaults.
- Compliance: Natural language processing helps AI review commercial agreements and covenants for compliance monitoring during loans. This automates time-consuming legal review.
|Lending Process||Key AI Techniques||Companies Offering Solutions|
|Retail document processing||OCR, NLP||Upstart, ZestFinance|
|Commercial risk assessment||Predictive modeling, NLP||Moody‘s Analytics, S&P Global|
|Credit decisioning||Predictive models, optimization||ZestFinance, Upstart|
|Portfolio monitoring||Anomaly detection, clustering||OnDeck, LendingClub|
AI is transforming investment processes like trading, research, and advisory:
- Robo-advisors like Betterment and Wealthfront use AI chatbots to provide 24/7 personalized investment recommendations tailored to each user‘s financial goals and risk tolerance.
- AI analyzes economic indicators, market conditions, and individual portfolios to manage and rebalance investments. This automation can lower management fees up to 70% .
- Hybrid models combine AI with human advisors for complex financial planning needs like trusts and tax optimization. This balances automation with human expertise.
- AI techniques like machine learning, neural networks, and natural language processing predict price movements by analyzing vast amounts of market data, news, and sentiment faster than humans.
- AI trading algorithms can react in nanoseconds to capitalize on opportunities. AI‘s emotionless, data-driven approach also avoids bias and human panic that lead to irrational decisions.
- According to JP Morgan, AI could lead to positive alpha generation of as much as $300 billion in earnings for institutions between 2018-2023 . However, many firms are still working to successfully implement AI trading.
|Investing Process||Key AI Techniques||Companies Offering Solutions|
|Robo-advisory||Recommendation engines, NLP, predictive analytics||Betterment, Wealthfront, SoFi|
|Algorithmic trading||Predictive modeling, sentiment analysis, pattern recognition||QuantConnect, Numer.ai|
AI is automating critical back-office financial operations:
- Natural language processing analyzes interactions and account data to determine the optimal messaging and strategies for collecting past due accounts. This balances recovery with customer satisfaction.
- According to Brighterion, their AI approach decreased delinquency rates by 76% compared to manual processes.
- AI-enabled invoice capture and document processing technologies extract key details 10x faster than manual data entry .
- Smart billing systems use natural language generation to send optimized payment reminders. This accelerates receivables collection and improves working capital.
- According to JP Morgan, AI automation could reduce finance headcount by 25% by 2025 through procure-to-pay automation .
- AI reconciles accounts across disparate systems 80% faster than manual reconciliation according to Ernst & Young .
- Natural language processing and logic-based algorithms extract transaction details from unstructured formats like bank statements and emails.
- This automation minimizes costly errors and frees finance staff for more value-added analysis.
|Process||Key AI Techniques||Companies Offering Solutions|
|Debt collection||NLP, predictive modeling||TrueAccord, Katabat|
|Invoice processing||Document extraction, OCR||Rossum, HyperScience|
|Account reconciliation||NLP, rules-based algorithms||Botkeeper, MindBridge|
Key insurance processes enhanced by AI include:
- Predictive models factor in thousands of data points to evaluate risk and determine optimal pricing down to the individual level. This balances profitability with customer satisfaction.
- According to BCG, AI can improve loss ratio by up to 30% through personalized pricing .
- Computer vision AI accurately classifies claims by analyzing photos of property damage. This cuts claims processing time by 10x .
- Natural language processing of claims documents and call center transcripts flags coverage issues and fraud indicators for investigation.
- Chatbots like Allstate‘s Amelia handle routine inquiries to improve customer satisfaction and contact center efficiency .
- Speech analytics determine caller sentiment and guide agents for optimal service and upsell suggestions.
|Insurance Process||Key AI Techniques||Companies Offering Solutions|
|Pricing||Predictive modeling, optimization||Cape Analytics, Planck Data|
|Claims||Computer vision, NLP, voice||Lemonade, Trov, Tractable|
|Customer service||NLP chatbots, speech analytics||Allstate, Geico, Root Insurance|
Audit & Compliance
AI and machine learning techniques are transforming fraud detection and regulatory processes:
- Unsupervised anomaly detection identifies outliers indicative of fraud across millions of transactions in real-time. This enables early intervention before significant losses.
- AI analyzes relationships between entities to uncover complex fraud networks that evade rule-based systems.
- According to McKinsey, AI fraud prevention solutions have demonstrated over 50% improvement in fraud detection rates .
- Natural language processing scans regulatory filings, contracts, and internal communications for risks across thousands of pages faster than human review.
- Ongoing monitoring ensures issues are flagged early before significant liability or reputation damage.
- Big tech companies like AWS offer AI-powered compliance solutions tailored for industries like finance and healthcare with pre-trained models .
Travel & Expense Management
- Optical character recognition extracts key details from receipts and expense reports to check for policy compliance.
- Natural language processing flags unusual spending while pattern recognition identifies duplicate claims.
- Case studies show up to 25% improvement in report accuracy using AI for expense management .
|Compliance Process||Key AI Techniques||Companies Offering Solutions|
|Fraud detection||Anomaly detection, graph analysis, NLP||Featurespace, Feedzai, Quantexa|
|Regulatory monitoring||NLP, text classification||AWS, Microsoft, IBM|
|Expense reporting||OCR, NLP, rules engines||AppZen, PairSoft|
Interactions with customers and prospects can be enhanced using AI:
Know Your Customer (KYC) Processes
- Natural language processing analyzes documents, news, and other unstructured data to identify anomalies and risk factors in client screening. This improves efficiency and accuracy.
- According to McKinsey, AI techniques can reduce KYC screening costs by up to 90% and slash processing time from weeks to hours .
Responding to Inquiries
- Chatbots use natural language processing to understand questions and provide relevant answers instantly 24/7. This improves satisfaction while reducing call volumes.
- For complex inquiries, AI recommends optimal content to equip agents with the right information during the interaction.
Upsell & Cross-sell Recommendations
- Deep learning algorithms analyze customer data to determine life events and suggest relevant products. Identifying new parents for daycare discounts increased conversion 38% for USAA .
- AI call analysis solutions like Chorus.ai guide agents with prompts to maximize upsell conversions.
- Machine learning models identify customers at high risk of churn based on behaviors, spending patterns, demographics, and other attributes. Proactive retention is 4-5x more effective after churn begins .
- AI pinpoints leading indicators and reasons for churn so corrective actions can be taken. This improves retention.
|Customer Process||Key AI Techniques||Companies Offering Solutions|
|KYC||NLP, anomaly detection||Jumio, Onfido, Socure|
|Conversational AI||NLP chatbots||Kasisto, Finn.ai, Clinc|
|Upsell recommendations||Deep learning, NLP||PROS, InMoment, ThoughtSpot|
|Churn prediction||Predictive modeling, feature engineering||Totango, CustomerGauge, ChurnZero|
Additional Use Cases
Beyond the major categories covered, here are some other promising AI finance applications:
Quantitative Investing – AI runs complex simulations to create optimized portfolios mathematically proven to maximize return at every risk level. Companies like WorldQuant and Two Sigma use these techniques.
Alternative Data – AI derives insights from satellite images, credit card transactions, and other unstructured data sources to assess risk or detect fraud signals invisible in traditional data.
Model Risk Management – As finance relies more on data models, AI techniques like LIME and Shapley values explain model behavior and quantify fairness. This is critical for compliance.
Real-Time Risk – AI conducts micro-simulations of portfolios across massive scenarios to quantify risk exposure in real-time. This enables better hedging decisions.
Contract Analytics – NLP scans agreements to extract key terms and obligations for tracking. This is far faster than manual review and ensures compliance.
The Future of AI in Finance
As this overview illustrates, AI is already transforming major financial processes – from lending and investments to operations and customer engagement. While thoughtful and responsible implementation is critical, the benefits AI can offer include:
- Increased efficiency and lower costs through automation
- Improved risk management and compliance
- Faster processes leading to better customer experiences
- More data-driven insights for strategic decision making
According to a survey by the Oracle and EBA, 64% of banks are already experiencing a positive ROI from their AI investments . And nearly 3 in 4 financial institutions believe AI will have a significant impact on competitiveness going forward . Companies who thoughtfully embrace AI today will gain an advantage shaping the future of financial services.