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If we investigate the major disruptions in today’s world of finance, we find that Fintech is an innovation that has completely changed the modus operandi of banking.

The brick-and-mortar banking system is gradually being transformed into a handheld device.  When the marginalized population gets access to finance, the broader economic goal of financial inclusion or poverty reduction of the government gets addressed - it unleashes the true potency to reach the un-bankable to the banking fraternity, bringing economies of scale and reducing search and transaction costs. Numerous fintech companies have transformed by embracing the values of human-centered design as a framework to balance the needs of the organization with the needs of its users, customers, and communities. They are now present across the value chain- from capital raising services to payment services to investment management services, as well as insurance. 

The entire ecosystem has been possible through the integration of Artificial Intelligence and blockchain technology, and now a probable question is why AI is so critical for fintech. The reason could be behind the dynamic nature of the problem, as it is ever-evolving. Fintech tries to bring to the table financial solutions in a more organized manner, and AI is the architect who builds the matter by weaving across information. 

As we all know, any financial transaction is bound by legal formalities, and it is of utmost importance to secure the transaction through proper legal paperwork. Fintechs have brought paperless transactions- earlier legal papers needed to be signed physically. Presently, signatures are becoming digitalized. Voice-enabled transactions are getting embedded. The present trend of smart contracts is making things easier as well as complex for financing institutions. 

All AI methods are always at the juncture of the usage of humans. The moment the intervention of humans takes place, there are chances of misusing the information. So, in a way, the data that gives transparency, on the other side, can become the food for anomalies or discrepancies. Like the question, Karna faced when fighting against his half-brothers. These unethical practices loom large in the financial industry. We look at some of the issues which have huge monetary implications and people tend to take advantage of the gaps in the legal system. 

Scam Detection 

How it can work 

This represents an unethically designed and planned transaction that uses deception to siphon money with the help of systems by creating the wrong identity and associated documents. The ongoing complexity and continuous efforts for innovation of financial products raise additional avenues for financial scams that impact thousands of investors to lose money in hedge funds, Ponzi schemes, currency trading, virtual currency, working capital requirements, and many other schemes that damage investors. 

Combining supervised and unsupervised machine learning as part of AI fraud detection strategy can enable digital finance to detect complex frauds. The speed at which the sophistication and scale of fraud attacks are changing is imperative now that legal terminologies and detection of legal frauds need to bring in disruptive models. When we talk about associated documents, the clauses and terms and conditions of the associated docs can be brought to the forefront through Ethical AI. Keyword searches and searches with similar IDs can only give where the anomaly exists, while supervised and unsupervised AI can find the path to detect fraud. Just like financial statement analysis, there is a need to automate the analysis of legal terms. 

The ethical use of AI can significantly enhance legal contextualization in fintechs by ensuring fairness, transparency, and accountability in their operations.

  • Clarity in credit decisions:

AI algorithms can be programmed to make equitable lending decisions by evaluating creditworthiness using a diverse set of unbiased factors. Ethical AI guarantees that these decisions remain unaffected by factors such as race, gender, or other discriminatory attributes, thereby upholding fairness in financial transactions.

  • Compliance watchdog: 

Ethical AI systems have the capability to consistently observe and adjust to evolving regulations. Through real-time analysis of extensive legal documents and updates, AI can assist fintech companies in adhering to intricate and ever-changing legal frameworks, thereby decreasing the likelihood of legal problems and fines.

  • Anomaly detection: 

AI-driven algorithms can identify fraudulent activities by examining patterns and irregularities in real-time data. Ethical AI guarantees compliance with privacy and data protection laws while pinpointing and mitigating potential fraud, thus bolstering both legal adherence and customer confidence.

  • Data sovereignty:

Ethical AI models can protect customer data using sophisticated encryption and data anonymization methods. By ensuring rigorous compliance with data protection laws, fintech companies can prevent legal issues associated with data breaches and breaches of privacy.

  • Data transparency: 

Ethical AI algorithms are crafted to be transparent and explicable. This implies that the decisions reached by AI models can be traced back, allowing regulators and customers to comprehend the specific rationale behind those conclusions. This transparency is essential for legal accountability and building trust with customers.

  • Automating digital contracts:

AI-powered tools for contract analysis can rapidly scan and comprehend legal documents. This can assist fintech companies in grasping intricate legal agreements, ensuring they meet contractual obligations and prevent legal disputes.

  • Anti-money laundering:

AI systems can analyze extensive volumes of data to identify suspicious transactions, ensuring adherence to AML laws. Ethical AI in fintech guarantees precise recognition of money laundering risks while safeguarding customer privacy and adhering to legal guidelines.

  • Customer-centricity: 

AI-driven chatbots and virtual assistants can offer legal information to customers. In doing so, ethical AI ensures the advice given is accurate and complies with legal regulations, preventing the spread of misinformation and legal liabilities.

Embracing the ethical use of AI in fintech not only enhances efficiency and customer experience but also substantially strengthens legal contextualization by incorporating ethical AI principles. Thereby, fintechs can navigate the complex legal landscape with confidence and integrity.

Search through same legal identity search  

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Unfair trading practice 

Trading is a fundamental operational process for financial markets. This goes through several validations and checks before settlement. To enable malpractices in trading, several unfair means and misrepresentation of documents are done. Legal docs drafted unfairly and with dubious clauses can play a big fraudulent role. There have been many instances where unfair trading practices in the field of forex trade have brought in huge losses to the lenders. Fintechs who integrate statements of trading accounts across banks can trigger the anomalies. The transactions in trading accounts matching dates with transactions in bank accounts can find out commonalities, which can then trigger the questions on trading practices and unnatural growth/ degrowth in stock prices. The role of ethical AI comes into the picture, which can help detect human-centric issues. 

Detection through trading account statements of the customer

Transaction Fraud

Any transaction in the account that was not authorized directly by the card/account holder is considered to be a fraudulent transaction. But one could also consider as potentially fraudulent patterns like a business account hasn’t had any credit transactions in the last 15 or 30 days or even payments that are in oddly rounded numbers such as multiples of 100.  Payment to third parties/payments on loan transfers through dubious accounts can give indications on fraudulent transactions. 

Detection of Fraudulent Transactions through payments 

Frauds are connected with behavioral issues

Any deviation from regular programming could raise a behavioral red flag. If a potential borrower has installed/uninstalled lending apps in a window of, say, two months, or they’ve spent more than they usually do, or received more cash deposits than their usual salary credit can raise alarms on a well-trained machine learning model. A behavioral fraud then acts as an alarm for fraudulent activity and/or incoming delinquency. 

Detection through downloads in Google play services  

AI is the only way to detect frauds of large magnitude, and platforms built on these should be able to handle large volumes of past data. Supervised machine learning algorithms can look at transaction data like – common directorships, pending legal cases, nature of legal cases, address similarity, charges filed, etc, to minimize false positives and provide extremely fast responses to inquiries. Also, unsupervised machine learning can trigger new, more sophisticated forms of fraud. All these will help in the prevention of the lender's fund fraudulent companies, and the tribunals will be able to make justified decisions.  AI needs to be equipped to solve grave fraudulent transactions.

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