Introduction to Artificial Intelligence in FinTech.pdf
- 1. Artificial Intelligence in FinTech Prepared by: Asst. Prof. Nikul Zinzuvadiya
- 2. Where have you seen or used Artificial Intelligence today?
- 3. Raise your hand if you’ve ever used Google Pay, PhonePe, or invested through apps like Zerodha or Groww.
- 4. Everyday AI Examples: ● 📱 Face Unlock – Your phone recognizing your face ● 🗺 Google Maps – Suggesting the fastest route ● 🎬 YouTube / Netflix – Recommending videos based on your watch history ● 🛒 Amazon – Showing “products you may like” ● 💬 Chat GPT / Siri / Alexa – Talking like humans and giving smart answers AI is not just in robots or sci-fi movies. It’s already part of your life — you’re using it every single day!
- 5. What is Artificial Intelligence? Artificial Intelligence (AI) is the science of making machines think and act like humans. ● Can a computer play chess? ✅ ● Can your phone recognize your face? ✅ ● Can machines learn from data? ✅ That’s all AI!
- 6. Main Goals of AI ● Solve problems intelligently ● Make decisions like humans ● Learn from experience ● Communicate and understand language
- 7. Types of AI ● Narrow AI: Performs one task really well (e.g., Alexa, Siri) ● General AI: Can think like a human (still in theory) ● Super AI: Smarter than humans (conceptual)
- 8. Core components of AI ● Learning (via data) ● Reasoning (logic and decision-making) ● Self-correction (improvement over time)
- 9. Why AI Matters in FinTech? Data and Speed: ● AI processes millions of transactions per second ● FinTech is data-rich: transactions, credit history, payments, customer queries AI helps in: ● Real-time fraud detection ● Personalized product recommendations ● Loan eligibility prediction ● Risk-based pricing
- 10. Market Stats ● FinTech market: $34.1 billion (2023) ● AI's contribution: $44.8 billion ● Forecast by 2028: $49.49 billion
- 11. Key Technologies Machine Learning (ML): Machine Learning is a branch of Artificial Intelligence where computers learn from data and make decisions or predictions without being explicitly programmed for each specific task. Instead of writing manual rules for every situation, we give the system lots of data, and it finds patterns on its own.
- 12. Example : Fraud Detection by Mastercard Problem: Detecting fraud in real-time is hard. People use cards across countries, websites, devices — how do we tell what’s fraud? ML Solution: Mastercard uses machine learning to monitor millions of transactions per second. The ML model has been trained on: ● past fraud cases ● user behavior patterns ● transaction timings ● location and amount
- 13. When you swipe your card, the system quickly checks: ● Is the amount unusually high? ● Are you in a new location? ● Does it match your past behavior? If anything seems "off," the ML model flags it as suspicious and may block the transaction or alert the bank — within seconds. ✅ Benefit: ● Real-time fraud prevention ● Saves billions of dollars for banks and customers ● Keeps users safe without delays
- 14. Overall Impact of ML in FinTech Impact Area Description Fraud Detection Prevents fraud by catching unusual behavior instantly Credit Scoring Helps give loans to people without traditional credit history Investment Predicts stock trends and market risks Personal Finance Suggests savings plans and spending alerts
- 15. What is NLP? Natural Language Processing (NLP) is a part of Artificial Intelligence that helps computers read, understand, and respond to human language — whether written or spoken. NLP helps machines talk to us in our own language — like English, Hindi, or Bengali — and understand what we mean.
- 16. Example: Chatbots (e.g., SBI's SIA) Problem: Customers ask banks all kinds of questions 24x7 — like: ● “What is my account balance?” ● “How do I apply for a loan?” ● “Where is my nearest ATM?” NLP Solution: Banks like SBI use AI-based chatbots like SIA to handle these queries. The chatbot: ● Reads your message (NLP) ● Understands the meaning ● Finds the correct answer ● Replies in seconds
- 17. Predictive Analytics Predictive Analytics means using past data and current data to predict what might happen in the future.
- 18. How Does It Work? 1. Collect data (past transactions, customer behavior, trends) 2. Use statistical models or AI algorithms to find patterns 3. Make predictions like: ○ Will a customer pay back a loan? ○ Will the stock price go up or down? ○ What expenses will the customer have next month?
- 19. FinTech companies use this to: ● Reduce risk ● Offer better products ● Improve customer experience
- 20. Example: Predicting Loan Defaults Problem: Banks want to know if someone will repay a loan or miss payments. Predictive Analytics Solution: ● Looks at past data (age, salary, job type, past loans) ● Checks current data (spending habits, account balance) ● Predicts whether the person is likely to repay or likely to default Benefits: ● Helps banks avoid giving loans to high-risk customers ● Speeds up loan approvals for low-risk customers ● Reduces losses for financial institutions
- 21. Real-World Use Cases Use Case AI Feature Platform Virtual Assistant Chatbot/NLP Erica (Bank of America) handles 2 million queries/day Trading ML + Analytics BlackRock & Renaissance Tech for AI-powered trading Expense Automation RPA Expensify uses AI to scan bills & report Digital Onboarding Biometric + AI Apple Pay facial recognition Robo-Advisors ML + Analytics Wealthfront manages $25B portfolio
- 22. Chronological Timeline of AI in the Financial Sector 1950s–1960s: The Birth of AI (Theoretical Phase) ● Alan Turing proposed the Turing Test to assess machine intelligence (1950) ● AI was limited to rules and logic-based systems ● No real-world financial applications yet, but researchers dreamed of intelligent machines 1970s–1980s: Rule-Based Systems & Early Automation ● Banks started using rule-based expert systems for loan approvals and decision support ● Example: Basic credit scoring using fixed rules like income > ₹X, loan amount < ₹Y ● ATM introduction (1967 onwards) — early form of automated service delivery ✅ Impact: Automation entered finance; humans were still writing all the rules
- 23. 1990s: Data Explosion and Early AI in Trading ● Emergence of algorithmic trading on Wall Street ● Banks started using statistical models for market prediction ● Credit bureaus (like Experian) began applying basic pattern recognition for credit scoring ● AI was used for fraud detection (detecting unusual card activity) ✅ Impact: Financial decisions became more data-driven 2000s: Rise of Machine Learning ● Machine Learning (ML) started replacing rule-based systems ● AI models learned patterns from past customer behavior ● High-Frequency Trading (HFT) became common — using ML to analyze data in milliseconds ● Risk modeling and portfolio optimization improved dramatically ✅ Impact: Faster, smarter financial predictions
- 24. 2010s: AI Goes Mainstream in FinTech ● FinTech startups began using AI for: ○ Chatbots (like SBI's SIA, HDFC's EVA) ○ Robo-advisors (e.g., Zerodha, Upstox) ○ Alternate credit scoring using utility bills, phone usage, social behavior ● NLP and voice assistants started handling customer service ● AI became part of daily banking apps ✅ Impact: Finance became smarter, more customer-centric, and accessible
- 25. 2020–Present: AI + Big Data + Cloud + Blockchain ● AI now uses real-time data + cloud computing + deep learning ● Apps like Google Pay, PhonePe, Cred use AI to offer: ○ Smart offers ○ Fraud protection ○ Spending insights ● Banks use biometric AI (face, fingerprint) for secure transactions ● Predictive analytics is used for stock trends, personal finance tips ✅ Impact: AI is now essential to compete in the digital finance space
- 26. Year Milestone Description 1987 Expert system in loan approvals Early AI logic used in banks 1995 First use of AI in stock trading Quant funds used neural nets 2008 AI in fraud detection expands Post-financial crisis focus 2011 IBM Watson enters finance AI-based risk modeling 2016 Robo-advisors rise Personalized investment tools 2020 AI + COVID-19 AI used to assess financial risk during pandemic
- 27. What is Deep Learning? Deep Learning is a branch of Machine Learning that uses artificial neural networks with many layers (hence "deep") to learn complex patterns from large amounts of data. Deep Learning is self-learning, needs big data, and can make highly accurate predictions.
- 28. Basic Structure of Deep Learning Imagine a neural network with three types of layers: 1. Input Layer: Takes in raw data (e.g., a customer’s transaction history) 2. Hidden Layers: Multiple layers that extract and combine features 3. Output Layer: Gives final result (e.g., fraud or no fraud) 👉 The more hidden layers it has, the more “deep” the learning.
- 29. Why Deep Learning is Important in FinTech? FinTech deals with: ● Massive data: millions of transactions ● Complex decisions: fraud, credit, investments ● Real-time actions: alerts, approvals, forecasts Deep Learning can: ● Automatically extract features from raw financial data ● Detect very subtle patterns humans or simpler ML cannot ● Continuously improve as new data comes in
- 30. Real-World Applications of Deep Learning in FinTech 1. Fraud Detection ● DL models analyze millions of transaction patterns ● Can detect even small anomalies or new fraud types ● Learns continuously from new fraud cases 🔍 Example: Visa and Mastercard use deep neural networks for fraud prevention 2. Algorithmic Trading ● Uses time series data to predict stock movements ● Deep learning models like LSTM (Long Short-Term Memory) are used for financial forecasting 📈 Example: Hedge funds use DL to make split-second trading decisions
- 31. 3. Credit Scoring ● Deep learning looks beyond CIBIL score ● Considers non-traditional factors: transaction data, social behavior, digital footprint 🏦 Example: ZestMoney, Upstart — give loans to new-to-credit customers 4. Chatbots and Voice Assistants (with NLP + DL) ● Deep learning improves chatbots like SBI’s SIA and ICICI’s iPal ● Enables context-aware and emotion-sensitive responses ● Powers voice banking and multilingual support 🎤 Example: HDFC’s chatbot EVA is powered by deep NLP models 5. Risk Management and Forecasting ● DL helps banks simulate future scenarios (e.g., "Will this customer default?") ● Helps in portfolio risk optimization and stress testing 📊 Example: BlackRock uses DL to evaluate investment risks globally
- 32. Advantages of Deep Learning in FinTech Advantage Benefit High accuracy More reliable fraud detection, predictions Automatic feature extraction No manual data engineering needed Adaptability Learns from new data, adapts to market changes Speed Can make decisions in real time
- 33. Challenges of Using Deep Learning Challenge Explanation Needs large data DL performs best with big, labeled datasets High computation cost Requires GPUs and cloud platforms Black box nature Difficult to explain how it made the decision
- 34. Robotic Process Automation (RPA) in Finance Robotic Process Automation (RPA) is a technology that uses software robots (bots) to automate repetitive, rule-based tasks usually performed by humans. These bots mimic human interactions with digital systems like typing, clicking, or reading data from screens. Think of RPA bots as "digital employees" that work 24/7 without breaks, fatigue, or errors.
- 35. Why RPA in Finance? Finance is filled with structured, repetitive tasks like invoice processing, reconciliation, auditing, data entry, etc. These tasks are: ● Time-consuming ● Prone to human error ● Cost-intensive RPA automates these processes to: ● Improve efficiency ● Reduce costs ● Ensure accuracy
- 36. How RPA Works in Finance (Step-by-Step) Step Description 1. Input RPA receives structured data (Excel files, PDFs, emails) 2. Trigger A rule or event triggers the bot (e.g., file uploaded) 3. Action Bot performs the task (login, copy-paste, calculate) 4. Output Results are saved to databases, sent via email, or updated in ERP
- 37. Key Use Cases of RPA in Finance Use Case Description Accounts Payable Bots scan invoices, validate against POs, and process payments Accounts Receivable Automate customer reminders, generate invoices, track receipts Bank Reconciliation Match transactions from bank statements with internal ledgers Regulatory Compliance Automatically gather data, prepare compliance reports (e.g., GST, SOX) Payroll Processing Extract timesheets, calculate salaries, deduct taxes Financial Reporting Generate monthly/quarterly reports using real-time data
- 38. Benefits of RPA in Finance Benefit Explanation Increased Accuracy Bots eliminate manual errors Cost Savings Reduce manpower cost for routine tasks Faster Processing Tasks completed in seconds or minutes Improved Compliance Audit trails are automatically maintained Scalability Can run multiple bots in parallel during peak periods
- 39. Popular RPA Tools Tool Key Features UiPath User-friendly, drag-and-drop interface Automation Anywhere Enterprise-grade automation with analytics Blue Prism Focused on large-scale deployments Power Automate (Microsoft) Integrates easily with Microsoft 365 WorkFusion Includes AI-powered bots with ML capability
- 40. Real-Life Example Scenario: Invoice Processing ● A company receives 1000+ vendor invoices monthly. ● Previously done manually → took 2–3 weeks with errors. ● After RPA: Bot reads invoices (PDF), matches with POs, enters data into ERP, and emails confirmation to vendors → Completed in 2–3 hours with 100% accuracy.
- 41. Discuss the current trends and future prospects of Artificial Intelligence (AI) in the FinTech industry. What is FinTech? FinTech stands for Financial Technology. It refers to the use of modern technology (like mobile apps, software, or data analysis) to provide better financial services – such as online banking, digital wallets, investment apps, and loan platforms. What is Artificial Intelligence (AI)? AI is a branch of computer science that allows machines to think and learn like humans. It helps software make smart decisions by learning from data. Why AI is Important in FinTech? AI helps FinTech companies to: ● Make services faster, Reduce fraud, Give better advice to customers, Work more efficiently without needing many employees
- 42. Current Trends in AI in FinTech 1. Personalized Financial Advice ● AI looks at your spending habits and gives smart suggestions like how to save money or where to invest. ● Example: Investment apps (like robo-advisors) give you advice without needing a human financial advisor. 2. Fraud Detection ● AI can quickly find unusual activity in your bank account and block fraud. ● Example: If someone tries to use your card in another country suddenly, AI can stop the transaction. 3. Credit Scoring Using Non-Traditional Data ● Even if a person has no credit history, AI can check their phone usage, bill payments, and online behavior to decide if they are trustworthy for a loan.
- 43. 4. Chatbots and Virtual Assistants ● Many banks use AI chatbots to answer questions, help with transactions, and provide support 24/7. ● Example: Bank of America’s chatbot “Erica”. 5. Trading and Investment ● AI can study stock market trends and help investors make better decisions. ● Some AI systems even trade automatically without human input. 6. Regulatory Compliance ● AI helps financial companies follow government rules by checking thousands of transactions for illegal or suspicious activity.
- 44. Future Prospects of AI in FinTech 1. Fully AI-Driven Digital Banks ● In the future, there might be banks run mostly by AI – without any human employees. ● Customers can open accounts, get loans, or invest with just a few taps. 2. Quantum AI ● In the long term, AI combined with quantum computing will solve complex financial problems much faster. 3. AI in Decentralized Finance (DeFi) ● AI will be used in blockchain-based financial systems where smart contracts run without middlemen like banks. 4. Emotion-Sensitive AI ● Future apps may read your emotions through voice or face and suggest better financial choices when you're stressed or excited. 5. Bias-Free and Ethical AI ● Companies will work on making sure AI doesn’t treat people unfairly based on race, gender, or location. 6. AI for Sustainable Investing (ESG) ● AI will help investors choose companies that are good for the environment and society by analyzing ESG data.
- 45. AI’s Impact on the FinTech Ecosystem Objective: Understand how Artificial Intelligence (AI) is transforming the FinTech industry, including use cases, benefits, risks, ethical considerations, and future directions.
- 46. Introduction: Why AI Matters in FinTech FinTech—the fusion of finance and technology—is built on innovation. Among these technologies, AI is the most transformative, enabling smarter, faster, and more personalized financial services. Historically, financial services adopted AI early: ● 1990s: Neural networks processed 10–20% of U.S. checks via OCR. ● 2000s: AI used for credit scoring and fraud detection. ● 2020s: GenAI, conversational interfaces, and embedded finance are redefining user experience. AI is no longer optional; it’s imperative. Executives are pushing teams to “be bolder” with AI, not just experiment.
- 47. Ethical AI in FinTech: A Framework AI’s power must be guided by ethical principles. Below is a framework investment managers and FinTechs can use: a. Set Clear Objectives ● Define what problem AI is solving. ● Align with client and regulatory expectations. b. Use the Right Data ● Avoid biased or incomplete datasets. ● Ensure diversity and representativeness. c. Ensure Explainability ● AI must be explainable to regulators and clients. ● Tools like Explainable AI (XAI) help decode "black-box" models.
- 48. d. Maintain Accountability ● Define responsibility for AI decisions. ● Set up systems to track, audit, and correct AI outputs. e. Foster Responsible Innovation ● Encourage experimentation, but within guardrails. ● Ensure all innovation is inclusive and client-first. f. Mind Societal Impact ● Understand broader implications (e.g., exclusion due to AI bias). ● AI should promote financial inclusion, not widen gaps. g. Respect Individual Rights ● Transparent data collection and usage. ● Robust cybersecurity and privacy measures.
- 49. What’s Changed, What Hasn’t What’s Stayed the Same: ● Heavy regulation: AI must meet the same compliance standards as legacy systems. ● Customer needs: Time-saving, better returns, and trust still matter most. ● Need for explainability: All decisions must be auditable. What’s Changed: ● Tools are more powerful: GenAI and LLMs have democratized AI capabilities. ● Shift in leadership mindset: AI is now a strategic board-level concern. ● AI is accessible: Previously niche tools are now widespread across departments.
- 50. Opportunities & Competitive Advantage AI can be split into: Everyday AI: ● Productivity gains (e.g., customer service automation, back-office tasks). ● Efficiency tools—important, but replicable. Game-Changing AI: ● Conversational AI replacing traditional client interfaces. ● Embedded finance expanding credit to underserved populations. ● Credit scoring using non-traditional data (e.g., phone usage, payment behavior). “Nobody gets a dopamine hit from visiting a bank branch. AI must make finance invisible, seamless, and personal.” — Panel insight
- 51. Risks & Concerns a. AI Hallucinations ● LLMs can generate false or misleading information—risky in a regulated sector. b. Data Privacy & Trust ● You don’t “own” customer data—you’re entrusted with it. c. Skills Gap ● Not just hiring new talent—upskilling the existing workforce is critical. ● Firms must democratize AI knowledge. d. Over-Hype & Unrealistic Expectations ● Not everything should be “AI-ed.” ● Leaders must balance boldness with caution.
- 52. Future Outlook: Where Is This Going? Short-term: ● More industrial-scale AI deployment. ● AI becomes as common as “digital transformation” was 5 years ago. Medium-term: ● Conversational AI + embedded finance mainstream. ● Lending decisions based on behavior + alternative data, not just credit scores. Long-term: ● AI becomes the invisible operating layer of all financial services. ● Entirely autonomous financial advisors or digital banks could emerge.
- 53. Case Study: Zest AI – Ethical and Inclusive Credit Scoring with AI Case Overview Company: Zest AI Founded: 2009 (originally ZestFinance) Industry: FinTech – Credit Risk Management HQ: Los Angeles, USA Main AI Application: Machine learning-based credit underwriting models for lenders
- 54. Case Context Traditional credit scoring systems, such as FICO, rely heavily on historical credit data (repayment history, outstanding debts, etc.). This system excludes millions of "credit invisibles"—people who don’t have enough credit history to generate a score. This leads to: ● Missed opportunities for lenders ● Financial exclusion of low-income and minority borrowers Zest AI developed machine learning models to evaluate creditworthiness using alternative data, with the goal of making lending more fair, inclusive, and accurate.
- 55. Zest AI’s AI-Driven Approach 1. Data Collection & Feature Engineering ● Uses thousands of data points, not just traditional credit bureau inputs. ● Includes rent payments, employment history, education, and more. ● AI models detect subtle patterns that predict default risk better than traditional methods. 2. Model Development ● Built with explainability tools that make decisions understandable to risk managers and regulators. ● Uses bias detection mechanisms to ensure compliance with fair lending laws. 3. Deployment ● Works with banks, credit unions, and auto lenders to integrate these models into loan approval systems. ● Offers APIs and dashboards for seamless integration into underwriting workflows.
- 56. Impact Business Outcomes ● 15–20% more approvals with no increase in risk. ● Lower default rates than with traditional credit models. ● Wider market reach — helps lenders serve younger, immigrant, or unbanked populations. Social Outcomes ● More equitable access to credit. ● Reduces systemic bias in financial systems. Regulatory & Ethical Edge ● Zest AI has advocated for “Explainable AI” and aligned its practices with the U.S. Equal Credit Opportunity Act (ECOA). ● Provides fair lending compliance reports with each model.
- 57. Ethical & Governance Challenges ❗ Challenge 1: Algorithmic Bias ● Even alternative data can reflect societal bias (e.g., ZIP codes can correlate with race/income). ● Risk of disparate impact even if race is not an input. Zest’s Response: Bias-auditing tools, regular fairness testing, and removal of proxy variables. ❗ Challenge 2: Explainability ● Financial institutions are required to give customers “reasons” for credit denial. ● Many AI models are black boxes, making explainability hard. Zest’s Response: Uses “Glassbox” AI tools to trace decisions and present human-readable justifications. ❗ Challenge 3: Regulation ● Regulators are cautious about ML in lending. ● Explainability and audit trails are critical for approval. Zest’s Response: Actively works with regulators and offers transparent documentation.
- 58. Ethical Issues in AI Applications 1. Bias and Discrimination: What’s the Problem? AI systems learn from data — and if that data includes unfair patterns from the past (like racism or sexism), the AI can repeat or even make those biases worse. Examples: ● Credit Cards: Some AI models used by banks or credit card companies may give lower credit limits to women or minorities, even if they have the same or better financial background than others. (Example: Apple Card controversy – men were getting higher credit limits than women.) ● Facial Recognition: Some AI face-recognition tools work less accurately on people with darker skin tones, which can lead to misidentification or unfair treatment.
- 59. Apple Card controversy The Apple Card controversy refers to a 2019 incident where Goldman Sachs, the issuer of the Apple Card, was accused of gender discrimination in its credit limit decisions — highlighting the bias and discrimination risks in AI-powered financial services. What Happened? ● Multiple users, including tech entrepreneur David Heinemeier Hansson, publicly complained that Apple Card gave significantly higher credit limits to men than to women, even when the women had higher credit scores or shared bank accounts with the men. ● In one case, Hansson said he received 20x the credit limit compared to his wife, despite her having a better credit score and financial standing. ● Apple co-founder Steve Wozniak echoed similar concerns about the disparity between his and his wife's credit limits.
- 60. Why It Matters for AI Ethics: Goldman Sachs used an algorithmic underwriting model to determine credit limits. The controversy raised concerns that: ● The model may have been trained on biased historical data. ● Gender might not have been explicitly used, but proxy variables (e.g., income patterns, spending behavior, or even ZIP codes) could have led to indirect discrimination. Aftermath: ● The New York State Department of Financial Services (NYDFS) opened an investigation. ● Goldman Sachs denied intentional discrimination, stating the algorithm was gender-neutral. ● However, the case highlighted how algorithmic opacity and unintentional bias can result in unethical outcomes, even when legal compliance is claimed.
- 61. Why This Matters in FinTech: ● Credit Underwriting: FinTech companies like Zest AI or Upstart use AI to decide who gets loans. If their AI is biased, it might deny loans unfairly. ● Fraud Detection: Some fraud systems flag more people from certain communities by mistake, causing unnecessary trouble for those individuals. How to Fix It (Mitigation): ● Use fair algorithms that try to treat everyone equally. ● Do regular checks (called "bias audits") to see if the AI is treating people unfairly. ● Avoid using data that could act as a “hidden” signal for race or gender — like ZIP code, which may reflect where different communities live.
- 62. 2.Lack of Explainability – “The Black Box” Problem: What’s the Problem? Some AI systems — especially complex ones like deep learning models — make decisions, but we don’t always understand how or why they made those decisions. It’s like a black box: you put in data and get a result, but what happens inside is a mystery. Examples: ● Loan Rejection: Imagine applying for a loan and getting rejected, but the bank can’t explain the reason clearly — only that “the AI said no.” ● Stock Trading: Some financial companies use AI to trade stocks. These algorithms make fast decisions that even their creators don’t fully understand.
- 63. Why This Matters in FinTech: ● Regulations: Laws like the Equal Credit Opportunity Act (USA) or GDPR (EU) require companies to explain their decisions — especially when they deny people credit or services. ● Customer Trust: People want to know why an AI made a decision about their money. If there's no clear reason, they lose trust in the system. How to Fix It (Mitigation): ● Use Explainable AI (XAI) – models that show how decisions were made. ● Where possible, use simpler models that are easier to understand (like decision trees instead of deep neural networks). ● Give users clear and simple explanations — like “Your credit score was too low” or “You don’t meet the income requirement” — not just technical outputs.
- 64. 3. Data Privacy and Consent: What’s the Problem? AI systems often need a lot of personal data — like your spending habits, location, or phone use. Sometimes, this data is collected without telling people clearly or without their proper consent. This raises concerns about privacy and trust. Examples: ● Finance Apps: Some apps collect your financial data (like how much you spend or save) without explaining it clearly. ● Insurance AI: Some insurance companies use your phone’s location or behavior data to decide your insurance rates — often without you fully realizing it.
- 65. Why This Matters in FinTech: ● In areas like open banking, credit scoring, and robo-advisors, AI uses sensitive financial info. ● If companies misuse this data, they can face legal trouble or lose customer trust. How to Fix It (Mitigation): ● Use privacy-by-design: Build systems that protect privacy from the start. ● Only collect what’s necessary, and use data encryption to keep it safe. ● Let users clearly choose whether they want to share their data — with opt-in and opt-out options that are easy to understand.
- 66. 4. Autonomy and Accountability What’s the Problem? AI is now making important decisions — like approving loans or managing investments. But if something goes wrong, who is responsible? AI isn’t a person or a legal entity — so it can’t be held accountable. 💬 Examples: ● Robo-advisors (AI financial advisors) might make risky investments, and customers can lose money. ● An AI system could deny a mortgage or charge someone the wrong interest rate, and no one knows who to blame.
- 67. Why This Matters in FinTech: ● In automated trading, AI can cause flash crashes (sudden market drops). ● Loan approvals and fraud detection are now automated — but what happens if the AI makes a mistake? How to Fix It (Mitigation): ● Use “human-in-the-loop” systems — let people review or override important AI decisions. ● Set up clear steps (called escalation protocols) for what to do if the AI gets it wrong. ● Make sure a person or team is legally and operationally responsible for the AI’s actions.
- 68. 5. Over-Reliance on AI (Losing Skills & Increasing Risk) What’s the Problem? Sometimes, companies trust AI too much. They may stop using human judgment — even when AI makes mistakes. This can lead to bigger problems if something goes wrong. Examples: ● An AI model fails during a market crisis, but no one knows what to do because they relied only on the AI. ● A customer complains about a wrong decision, but staff just say, “The system says it’s correct” — without checking.
- 69. Why This Matters in FinTech: ● In areas like portfolio management, fraud detection, and real-time credit approvals, blindly trusting AI can lead to bad decisions or missed problems. ● Also, when staff stop making decisions themselves, they can lose important skills. How to Fix It (Mitigation): ● Make sure humans regularly review AI decisions. ● Train staff to understand, question, and override AI outputs when needed.
- 70. 6. Societal Impact & Financial Inclusion What’s the Problem? AI in finance can either help more people get access to money services — or it can leave some people out, especially those from poor or underrepresented communities. If the AI isn’t designed carefully, it can make things worse for people who are already struggling. Examples: ● People in underbanked communities (who don’t use regular banks) may get denied loans because they don’t have the usual credit data. ● AI might set prices too high for low-income users, making services unaffordable.
- 71. Why This Matters in FinTech: ● FinTech tools like Buy Now Pay Later (BNPL), microloans, insurance pricing, and digital wallets are meant to help more people. ● But if AI isn't inclusive, these tools might exclude the people who need them most. How to Fix It (Mitigation): ● Design AI systems using inclusive principles — consider different incomes, locations, and life situations. ● Test the AI on a wide range of people (diverse backgrounds and communities) to make sure it works fairly for everyone.
- 72. Guiding Frameworks for Ethical AI These are rules and best practices created by governments and organizations to make sure AI is used responsibly and fairly — especially in important areas like finance. Global Guidelines: These are big international efforts to guide how AI should be developed and used: ● OECD Principles on AI: These say AI should be fair, transparent, and respect human rights. ● EU AI Act: A law in Europe that will control high-risk AI systems (like those used in banking or healthcare) to protect people. ● US AI Bill of Rights: A set of principles in the U.S. saying people should have privacy, transparency, and protection when AI makes decisions about them. ● IEEE Ethically Aligned Design: Technical guidance for engineers and companies to build AI systems that put people first.
- 73. What FinTech Companies Should Do: ● Set up AI governance boards: Create internal teams to oversee how AI is used — making sure it’s ethical and safe. ● Keep records of how AI works: Document how models were built, what data they use, and what risks they might have. This helps with accountability and audits. ● Do impact assessments before launch: Before using AI in real-world settings, check how it might affect people and communities — especially vulnerable groups.