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data analytics finance

Data analytics
for financial services

Data analytics teams utilize GiQ to transform data into actionable recommendations, significantly improving transaction reconciliation, fraud detection, risk assessment & credit scoring.

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Risk management & fraud detection

Discover implicit fraud signals and patterns

GiQ’s frequent pattern mining reveals hidden links in transaction data, helping financial institutions and insurers detect fraud early. By identifying recurring fraudulent behaviors such as credit card misuse or false insurance claims, financial organizations can quickly respond to potential threats. The graph-based approach uncovers patterns without the need for pre-trained models, accelerating the detection process and improving fraud prevention.

Detect anomalies for financial crimes and insurance fraud

GiQ’s anomaly detection provides continuous monitoring for both financial institutions and insurance companies, identifying unusual behaviors that signal potential fraud. From spotting unusual transaction flows to detecting irregularities in claim filings, GiQ flags suspicious activities as they occur. The graph structure adapts to evolving fraud schemes, ensuring that fraud detection is always accurate and timely.

Improve risk assessment accuracy with graph-based predictions

By analyzing customer behavior, transaction history, and market conditions with GiQ, financial institutions, and insurers can forecast risks with greater precision. The adaptive neural graph technology ensures that predictions stay relevant without the need for retraining, allowing for continuous risk management in changing environments.

Customer relationships & personalization

Deliver personalized product recommendations

GiQ’s similarity detection enables banks and insurers to offer highly personalized products, such as tailored loan offers or insurance policies, by identifying customers with similar financial behaviors or coverage needs. The graph-based approach reveals deep, non-obvious similarities between customers, which leads to more accurate and relevant product suggestions and ultimately improves customer satisfaction and retention.

Unlock cross-selling opportunities with association rule mining

GiQ uncovers valuable cross-selling opportunities, helping banks and insurance companies boost revenue. Discovering patterns where customers who hold certain financial products (e.g., savings accounts) are likely to also need complementary services (e.g., mortgage or home insurance) enables more targeted marketing. The graph approach ensures deeper insights into customer preferences and buying behavior, increasing the chances of successful cross-sales.

Operational efficiency & automation

Streamline transaction reconciliation with automated entity resolution

GiQ’s automated entity resolution simplifies transaction reconciliation by accurately matching records and resolving inconsistencies across customer databases. For banks, this enhances back-office efficiency, reducing manual work and operational costs. In insurance, it ensures that policyholder data is accurate, which leads to faster claim processing and improved customer satisfaction.

Boost financial forecasting accuracy with graph-based predictions

GiQ’s prediction engine leverages its neural graph technology to provide accurate financial forecasts for banks and insurers. By analyzing historical data, market trends, and interconnected factors, it helps organizations anticipate future cash flow, market shifts, or customer behavior. Unlike traditional forecasting methods, GiQ’s predictions automatically adapt to new data, delivering more reliable and timely forecasts without constant retraining.

Compliance & regulatory reporting

Ensure anti-money laundering (AML) compliance

GiQ’s anomaly detection enhances Anti-Money Laundering (AML) compliance by identifying suspicious transaction patterns that deviate from typical customer behavior. Financial institutions benefit from real-time alerts that ensure timely reporting and swift action against potential money laundering activities. The graph-based system automatically adapts to new money-laundering techniques, providing continuous protection without requiring frequent manual updates.

Proactively manage regulatory risks

GiQ’s frequent pattern mining helps financial institutions, and insurers stay ahead of regulatory risks by uncovering recurring issues in transaction data. Whether it's identifying common non-compliant behaviors or flagging high-risk activities, the graph-based approach visualizes hidden relationships within the data. This enables compliance teams to proactively address regulatory concerns, ensuring organizations stay aligned with evolving regulations.

FAQ: data analytics for finance, insurance & fintech

Discover expert answers to frequently asked questions about transaction reconciliation, credit scoring, and fraud detection.

What is transaction reconciliation?

Transaction reconciliation is the process of comparing internal financial records against external statements, such as bank statements, to ensure that they are consistent and accurate. This process helps identify discrepancies, errors, or fraudulent activities.

How does AI improve transaction reconciliation?

AI significantly enhances transaction reconciliation by automating the matching of transactions from various sources, thereby increasing efficiency and speed. It reduces human error by accurately detecting discrepancies and anomalies, which might indicate errors or fraudulent activities. AI's ability to process large volumes of transactions in real-time allows for continuous monitoring and timely reporting, providing businesses with up-to-date insights into their financial health. Additionally, AI's advanced data analysis capabilities can identify patterns and trends, offering valuable predictive insights that improve overall financial management.

How does fraud detection in financial transactions work?

Fraud detection in financial transactions works by analyzing transaction data to identify unusual patterns and anomalies that may indicate fraudulent activity. This process involves continuously monitoring transactions in real time and using predefined rules and historical data to flag suspicious activities. Techniques like predictive analytics help in identifying potential fraud before it occurs, enhancing security measures. Anomaly detection differentiates between normal and suspicious behavior, ensuring that genuine transactions are processed smoothly while intercepting fraudulent ones easily.

How do AI and LLMs help in fraud detection?

AI and LLMs can enhance fraud detection in financial transactions by leveraging advanced data analysis and predictive capabilities. AI algorithms can process vast amounts of transaction data in identifying subtle patterns and anomalies. LLMs can improve the understanding of transaction contexts and detect nuanced fraudulent behaviors through natural language processing, analyzing textual data such as transaction descriptions, customer communications, and other unstructured data.

How is banking using AI for credit scoring?

AI revolutionizes credit scoring by leveraging its ability to analyze extensive datasets with exceptional precision and speed. Unlike traditional credit scoring methods that rely primarily on limited financial history, AI integrates diverse data sources, including utility payments, social media activity, and online behavior, providing a more holistic view of an individual's creditworthiness. AI systems continuously learn and adapt, updating credit scoring models as new information becomes available, ensuring ongoing accuracy. This leads to fairer, more inclusive credit assessments, improved risk management for lenders, and faster credit application decisions.

Is there anything else?

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