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Oct 10 2023
The Benefits of Generative AI for Banking & Financial Leaders

Generative AI is a subset of Artificial Intelligence (AI) that focuses on creating artificial data or content. It uses deep learning algorithms to generate images, videos, or audio based on the data given to it. Instead of learning from data, generative AI creates brand-new data.

Generative AI is transforming data analytics in the financial services industry, presenting new opportunities to enhance customer service, increase revenue, improve security, reduce risks, optimize investments and strategic planning, and more. Here are some common uses and benefits of generative AI in financial services:

Chatbots: Banks can use generative AI to create chatbots that mimic human conversation through text or voice interactions. Using chatbots can improve customer service, cut costs, and boost revenue.  For example, chatbots can save banks money by automating routine customer service functions such as answering questions about account balances and performing routine tasks such as making transfers and sending messages. More advanced uses include providing personalized recommendations and sales based on a customer’s history and activity.

Fraud Detection and Prevention: Generative AI is supplementing traditional fraud analytics with models that can identify abnormal patterns in large volumes of financial transactions so that financial institutions can halt suspicious transactions faster. Financial companies are also using generative AI to create synthetic data that simulates fraud so they can develop more robust fraud detection algorithms.

Anti-Money Laundering: Using generative AI to analyze large volumes of financial data such as transactions, accounts, customer profiles, and company information. Know Your Customer (KYC) data can identify patterns and anomalies that may indicate money laundering activities.

Credit Risk Assessment: Generative AI models can determine credit risk more accurately and much faster by analyzing vast amounts of data, including financial statements, credit scores, transaction histories, and other relevant data. This can lead to better lending decisions that reduce credit risk.

Credit Reporting: Companies in the financial services industry can use generative AI to automatically create credit reports and other financial documents. This can streamline loan application and approval processes, reducing paperwork and improving efficiency.

Algorithmic Trading: Traders can use generative AI to potentially achieve higher returns. Generative AI helps develop trading algorithms that produce trading signals for when to buy or sell a security and that predict market movements.

Portfolio Management: Generative AI can help optimize portfolio allocations by generating asset combinations and simulating their performance. Portfolio managers can use this information to build efficient portfolios based on criteria such as risk tolerance and return objectives.

Asset Management: Businesses can use generative AI to analyze market data and forecast asset prices, interest rates, and other economic trends. This information is valuable for making investment decisions and managing financial assets. Generative AI excels in analyzing unstructured data, such as social media sentiments and news articles to help investment managers gain insights into investor perceptions and market shifts.

Strategic Planning: A company in financial services can leverage generative AI to develop predictive models for financial metrics such as customer churn, account balances, and revenue. Better forecasts of these metrics can improve strategic planning and resource allocation.

Generative AI and the Actian Data Platform

Generative AI is a versatile tool that presents many opportunities for data analytics within the financial service industry. However, generative AI requires the right data platform to be successful. The Actian Data Platform is the first as-a-service solution to unify analytics, transactions, and integration. Its flexible cloud, on-premises, and hybrid cloud architecture brings you trusted, real-time insights, making it easier to get from data source to decision with confidence. The Actian platform’s low, no-code integration with data quality and transformation options make it easier and more flexible to address more generative AI needs/use cases.

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Author: Teresa Wingfield

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Sep 14 2023
How to Use Financial Analytics to Detect Fraud

According to the Association of Certified Fraud Examiners, organizations lose 5% of their revenue to fraud each year. It’s no wonder that financial analytics for fraud detection is in such high demand given this alarming statistic. Fortunately, financial analytics can play a crucial role in helping businesses detect and prevent fraud by analyzing various patterns, discrepancies, and anomalies in financial data and flagging suspicious activities.

The list of use cases for fraud detection leveraging financial analytics seems to be endless, but here’s a breakdown by industry (banking and finance, healthcare, insurance, retail, and telecommunications) of some of the most common examples.

Banking and Finance

  • Credit Card Fraud: Flags credit card transactions that fall outside the scope of normal activity such as multiple transactions to one card in a short period of time, multiple rush orders to the same address, or an unusually high charge card amount.
  • Money Laundering: Analyzes transactions and the flow of funds across different accounts to identify suspicious activities such as structuring transactions to avoid reporting thresholds, layering funds through multiple accounts, or using complex transaction networks to obfuscate the source of funds.
  • Insider Trading: Identifies abnormal trading volumes, unusual price movements, and correlations between trading activities and significant corporate events.
  • Identity Theft: Flags accounts with unusual behavior such as sudden changes in spending patterns or unexpected transactions in new locations that may indicate that someone is illegally using another person’s data or account.

Healthcare

  • Fraudulent claims: Identifies claims with excessive or unnecessary procedures, and services that are inconsistent with a patient’s medical history.
  • Fraudulent billing: Spots unusual coding patterns, phantom billing, upcoding, unbundling, and disproportionate billing compared to peers.
  • Collusion: Analyzes claims and payment data to detect a high number of patient visits to different providers or patients who may be helping providers charge for tests they do not need.

Insurance

  • Application Fraud: Spots false information, fictitious beneficiaries, and agents opening and canceling policies to make quotas and bonuses.
  • Fraudulent Claims: Detects frequent or excessive claims, inflated claims, staged accidents, duplicate claims, and inconsistent information across claims.

Retail

  • Credit Card Fraud: Flags credit card transactions that fall outside the scope of normal activity, such as changes in the frequency of orders, higher orders than the average use transaction, changes to a shipping address, bulk orders for the same item, and unmatched or suspicious IP addresses.
  • Refund Fraud: Analyzes data such as the frequency and timing of returns, products returned and their value, and return reasons to discover potential fraud.

Telecommunications

  • Revenue Reporting Fraud: Examines billing data, contract terms, and revenue streams to identify discrepancies, such as unbilled services, underbilling, or revenue leakage that are likely to be due to fraudulent activities.
  • Subscriber Fraud: Analyzes subscriber behavior patterns and financial transactions to detect unusual account activities, such as frequent Subscriber Identity Module (SIM) card changes, abnormal roaming behavior, or suspicious calling patterns.

How Actian Can Help

Fraud is increasing both in frequency and amount. With so much at stake, businesses need to either adopt or ramp up their financial analytics to control fraud. Actian can assist you with a new project or help scale your existing analytics deployment. We are a trusted advisor with over 50 years of experience helping customers manage the world’s most critical data.

Actian makes financial data easy. We deliver cloud and on-premises data solutions that simplify how people connect, manage, and analyze data. We transform business by enabling customers to make confident, data-driven decisions that accelerate their organization’s growth. Our data platform integrates seamlessly, performs reliably, and delivers at industry-leading speeds.

The post How to Use Financial Analytics to Detect Fraud appeared first on Actian.


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Author: Teresa Wingfield

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