Hey everyone. I'm Manpo and I'm delighted to be here today to discuss advancing financial fraud detection with the graph databases. Before we dive into the topic, I would like to share a brief introduction about my background. I hold Master's degree in information technology and bring over 16 years of experience across various technology domains. My career started in database management and I have since expanded my expertise to include cloud architecture and motor technologies. Currently, I'm working as a lead cloud data need at a prominent financial institution where I focus on driving platform modernization initiatives with ai, power financial services. Welcome to our exploration of graph database applications in financial fraud detection. This presentation examine how graph based approaches are revolutionizing the identification and prevention of sophisticated fraud schemes. Over the next few minutes, we will dive into the following key areas. There are any capabilities in modeling complex relationships. We will explore why graph databases are particularly well suited. To present the indicate connection between entities in the financial or advanced algorithms travel for fraud detection. We'll discuss specific algorithms that leverage the graph structures to identify suspicious patterns, the integration of these databases. With the mission learning, we will examine how graph databases can be combined with the mission learning techniques. To create even more fourfold fraud detection systems and practical implementation of strategies for financial institutions, we'll consider the challenges and best practices for deploying graph database solutions in real financial setting. Let's begin by understanding graph databases. Now, unlike traditional relational databases that you might be familiar with, graph databases are structured around relationships. This is the fundamental shift in how we think about organizing and querying the data. Instead of tables, which organized data into row and columns, graph databases, users, notes, and edges notes represents the core entity in our data in a financial context. This could be like accounts, either it could be a checking account or savings account. Customers. Transactions, merchants and devices, IP addresses. This relationship centric structure mirrors the complicated interconnections inherent in financial ecosystems. Think about it in the real world, financial entities are not isolated. They're interconnected in complex ways. A customer may have multiple accounts, an account may be involved in many transactions, and a device might be used to access several different accounts. Graph databases allow us to model these complex relationships directly. This is a crucial difference from relational databases. While relational databases can represent relationships, then often struggles with complex queries that involved in tracing deep connections. These queries typically requires multiple giants, which can be competitionally expensive and slow. Especially with large data sets, craft databases server excel at addressing these relationships. They're designed from the ground up to efficiently navigate and can interconnected data. This makes them exceptionally powerful for uncovering sophisticated fraud patterns. Frauds often exploit these complex relationships. To hide their activities. For example, they might use a network of shell companies to launder money, or they might create fake identities to obtain loans. These type of schemes create intricate web of connections that are difficult to detect the traditional relational databases in the financial world. Accounts, customers, transactions, and devices become richly connected. Notes. This preserves the complex network topology of today's financial systems. By capturing these connections, we gain a much more complete and accurate picture of financial landscape. These representations enables investigators to efficiently follow money trails, identify suspicious patterns, and gain a holistic view of financial network. This level of insight is simply. Not possible when data is scattered across multiple tables in relational database. With graph databases, investigators can quickly and easily trace the flow of funds, identify anomalies, and uncover hidden relationships that would remain effective, invisible when analyzed using conventional tabular data structures. Building on our understanding of graph databases, let's now explore in more detail the power of relationship analysis. One of the key strengths of graph databases, licensing, their ability to uncover hidden database connections between seemingly unrelated entities. This capability is particularly crucial in the context of financial fraud detection. Frauds rarely operate in isolation. They typically engage in complex schemes that involves multiple individuals, accounts and transactions. Graph databases allows to move beyond a purely transactional way of financial activity. And instead, RRP network centric approach by representing financial entities as notes and their relationships as its, we can create a comprehensive map of financial landscape. By recognizing these patterns, investigators can quickly identify. Potentially fraud activity and prioritize their investigations accordingly. Disability to quickly and accurately identify suspicious patterns is a significant advantage in a fight against financial fraud, where a time is of innocence. Now let's dive into advanced graph algorithms that make graph databases so affect to in flood detection. These algorithms go beyond simple data retrieval and leverage the structure of graph to uncover hidden patterns and anomalies. Community detection. This family of algorithms focuses on identifying clusters are group of nodes that are densely connected within themself, but separately connected to nodes in their part of the graph. In a financial network, a community could represent a group of accounts that are frequently interacting with each other, potentially indicating a fraud ring or a money laundering operation. For example, imagine a scenario where several accounts are rapidly transforming funds among themselves, but they have very few transactions with external accounts. This pattern could suggest a. Close network of colliding actors, community detection algorithms can automatically identify such clusters, allowing investigators to quickly focus their attention on the most suspicious area of the graph. There are several different algorithms for community detection. With each, with these sworn strengths and weaknesses, centrality measures. These algorithms help us identifying the most important nodes in the network in the context of fraud. Detection important can have different meanings depending on the type of fraud we are looking for. Between a centrality, for instance, measures the number of times a node lies on the shortes path between two other nodes. Nodes are with high between a centrality access breaches in the network, connecting different communities or groups. In a money laundering scheme, a money mule who facilitates the transfer of funds between different actors would likely to have between a centrality. Degree Centrality, on the other hand, simply measures the number of connections a node has. A node with the high degree might be account that is involved in a large number of transactions, which could indicate either the list made high volume business or a hub of fraud, land activity path finding algorithms. These algorithms are used to find route or connections between nodes in the graph. They're particularly useful for tracing the flow of funds or assets in the complex financial transactions. Graph. Databases don't just throw data. They also enable powerful feature engineering techniques to significantly enhance machine lending models. Think of feature engineering as art of extracting most useful information from your data to feed into your AI and graphs, provide some incredibly rich information. Let's break down the key types of features we can derive from graph data, extracting network metrics. This is where we calculate various properties of nodes. Within the network, it's like understanding the role or importance of each entity in the graph, for example, degree simply counts how many connections a node has. A high degree accounts might be involved in many transactions, which could be normal or suspicious, depends on the context. Page rank famously used by Google. Gives the score for each node based on the number and importance of its incoming connections In a financial network, an account receives funds from many other high value accounts. Might be a central hub. We can look at pipelines. How many steps does it take for funds to move from account A to account B? Short paths might indicate direct transfers. While long, convoluted paths could suggest layering in money laundering, we can track the frequency of specific path patterns, our funds repeatedly throwing. Through the same sequence of accounts, this could reveal a structured money laundering process. And crucially, we can analyze the timing of fund flows, creating community features. This leverages the community detection algorithms we discussed earlier. We can determine the community size. Is an account part of large or small group. Large community might be money laundering networks. While small isolated accounts could be shell companies, we can measure community density. How tightly connected is the group? This are impressive. There's substantial improvements in fraud detection, accuracy, in some cases, up to 35% of improvement where the graph based features are added to the conventional models. This is huge forward in our ability to detect and prevent financial crime. Financial institutions operate on the scale that hard to grasp. For most of us, they process literally billions of transactions every single day. Think about critical transactions, money transfers, and stock rates. It's massive, constant flow of data. This presidency, huge challenge. For any database system and graph databases are no exception. To efficiently use graph databases in this environment, we need solutions that can truly scale. Scaling in this context isn't just storing the lot of data. It's about doing it efficiently and being able to query the data incredibly quickly. Here are some of the key techniques involved. Query optimization. This is where we make sure that our queries, our requests for information from the database are executing as efficiently as possible. Advancing index is crucial. Think of an index in your book. It helps you quickly find the information you need. Graph databases use sophisticated indexing techniques to locate specific nodes and relationships without having to scan the entire database. Caching is another important strategy. Frequently accessing data is stored in memory for ultrafast retrievals, so if you are repeatedly querying the same accounts are transactions, the system can provide the results almost instantly. Partitioning strategies, when your graph sets. Too big to fit in a single mission, you need to spread it across multiple computers. This is called partitioning. Sharding is common technique where we divide the graph into smaller pieces, our shards and store each chart on a different server, the distribute workload, and increase both storage capacity and process power distributed processing. To further speed up the query execution, we can perform graph competitions in parallel across distributed systems. Instead of the mission working on the query, multiple missions work on different parts of it simultaneously significantly reducing the overall processing time. On storage optimization. Finally, we need to use efficient data structures to store the graph data itself. Graph databases imply specializes storage formats that are optimized for traveling relationships. This is very difficult from how traditional relational databases store data and it's that allow. Graph databases to perform complex relationships queries so quickly. The good news is that leading vendors in graph database space, often enterprise grade solutions ally designed to handle these massive workloads, these systems can cap off touring trillions of edges, representing the connections between entities while still providing millisecond level query response times. These response is crucial for real time fraud detection. Streaming analytics for real time detection. This involves analyzing data as it's generated to identify suspicious activity immediately. Batch processing for deeper network analysis. This involves processing large amount of volumes, historical data to uncover complex fraud patterns that might not be apparent in real time. By combining these techniques, financial institutions can build robust and scalable fraud detection systems that can keep pace with the ever increasing volume and complex of financial transactions. Now let's shift gears and discuss graph neural networks, RGNS. If graph databases are foundations, gns are arguably the most cutting edge, a technique built on top of them for fraud detection. This is where things get really exact. Traditional Michel learning algorithms typically operate on data and tables, row and columns. To use them with graph data, we often have to do a lot of manual feature engineering. And as we discussed earlier, calculating network metrics, part based features and so on. This can be time consuming and require a lot of expertise. GNS on the other hand, are designed directly to process the draft structures itself. They can automatically learn meaningful representations from complex financial networks without us having to hand in any of these features. Think of an A, seeing the network in the same way we do, understanding the relationships and connections. A core concept of GNN is message passing neural networks. Imagine each node in the graph as a little messenger. It receives information from its neighbors, combines it with its own information, and then pass on it. This process repeats with information flowing through the network, allowing the GN Ns to learn the context of each within the neighborhood. What does this enable us to do? Gen are particularly powerful for node classifications and graph classifications. Comes to note classifications predicting whether a specific account our customer is fraudulent. Graph classifications predicting whether an anti-trans work is fraudulent and the results are compelling. Recent research demonstrate that GNS can achieve significantly higher fraud detection rates up to 20% higher than the traditional machine learning approaches. This is essentially true for detecting sophisticated fraud schemes that involve complex coordination between multiple entities. Furthermore, financial institutions that are implementing GN N based detection systems are reporting significant benefits reductions in false positives. This mean fear estimate transactions are incorrectly flagged as suspicious, saving time on resources for fraud, unleash improved detection for previously unidentified fraud patterns. GNS can uncover connections and anomalies that are traditional systems miss helping to stay ahead of evolving. Fraud tactics. In essence, GNS represents the shift in how we approach fraud detection. They move us from manually extracting features to automatically learn from the rich relational information within the financial network. Another real important technique in this space is called graph embeds. Now, this might sound a bit technical, but the core idea is quite interesting. Essentially, the graph embeds are a way to translate the complex structure of network into a format that traditional machine learning languages, algorithms. Can understand, think of as it's creating fingerprints for each node that capture its essential characteristics and relationships instead of representing a customer or an account as just a set of individual attributes. Graph embeds create dense vector representations. These vectors are like list of numbers that encode the North's position and roll within oral the network. Why this is useful because it allows us to cover the power filtration, mission learning. Things like clustering classification and anomaly detection while still taking into account the rich relational information contains. In the graph, there are several different techniques that creates graph membranes each with its sworn stance. Here are a few key ones not to weak. This method uses random works to explore the neighborhood of each node. Capturing both. Its local connections and its broader structural role in the network. It's good to identify nodes that are similar in terms of the network context. Graph auto encoders. These are neural networks that lend to compress the graph structures into lower dimensional representation and then reconstructed. They're effective at capturing the overall structure of the graph. Transi. This technique is particular useful for knowledge graphs where the relationship between entities have specific meaning. Transi preserves these relationships in the embedding space. Graph stage. This is an index lending approach, meaning it can generate embeddings for nodes it hasn't seen before. This is very valuable in dynamic financial networks where new nodes and transactions are constantly being added. So to recap, by converting graph data into vector representations, analyst can. Apply familiar mission learning techniques to identify suspicious patterns. For example, you could use clustering to group accounts with similar transaction behaviors or classifications to predict the likelihood of fraud for a given transaction. And there are significant practical benefits. Leading financial institutions report substantial speed improvements, often 40 to 60% of faster model training times when using embed based approaches compared to directly processing the graph data. This efficiency gain is the crucial in a fast phase environment where timely fraud detection is essential. A real world example of how this technology was used to uncover a sophisticated money laundering operation. It all started with initial detection. Ment systems might have missed it, but graph algorithms were able to identify suspicious transaction patterns. They involved accounts that on the surface seemed to be unrelated. However, the graph database revealed. Connections in their transaction behavior flagged them as potential involved in money laundering. They span across the financial ecosystems making them even harder to detect with the traditional methods. The graph databases shows that these accounts are connected by over 200 individual transactions, carefully structured and designed to obscure the flow of money and make it difficult to trace graph databases. Shows these accounts were connected by over 200 individual transactions. Carefully structured and designed to obscure the flaw of money and make it difficult to trace. This is where graph database accessibility to visualize and analyze relationships became crucial pattern analysis of this network expose classic money laundering techniques. Most notably layering. Layering is a process where money is moved through multiple accounts and transactions to distance it from its. Illicit source. The graph database clearly showed the funds moving rapidly through a series of accounts, often in complex sequences, making it incredibly difficult to follow the money trail using traditional methods. The temporal analysis capabilities of graph database, its ability to analyze the times of transactions were essentially in revealing this layering activity. So what was the investigation outcome? In short, a major success, the craft databases enable investigators. To piece together the entire Money LAU operation. This led to the recovery of $4.7 million in IC funds, a significant win for law enforcement and the financial institutions involved. But the impact went beyond just recovering the money. The investigation also led to identification of previously unknown criminal network. By mapping the relationship between account and individuals, the graph database provide crucial intelligence that helped to disman the entire operation. This case study. Demonstrates the power of graph databases In uncovering complex financial crime, it highlights their ability to see the connections that traditional systems miss, to visualize the complex networks and to analyze transactional patterns in the way they lead to successful investigations and significant recoveries. So to wrap up the practical side of things, let's recap the key implementation challenges and recommendation strategies for financial organizations considering graph databases for fraud detection. First, we have the data integration challenge as we discussed. Financial data is often scattered across various systems. You have got customer data here, transaction data there, account data somewhere else, getting all this data into Unified Graph database. The solution to this is to implement specialized ETL pipelines. ETL stands for Extract, transformation and Load. These pipelines are designed to pull data from these disparate systems, transform it into consistent format and load it into the graph database. But it's not just about moving the data, it's all about. The entity resolution. This is a crucial process of identifying and merging records that are referred to the same real world entities. For example, making sure that John Smith and j Smith are recognized as same person. This ensure the accuracy and completeness of the graph. Coming to the performance optimization challenge, graph databases, especially in the financial sector, can grow to enamor sizes containing billions of notes and edges. Ensuring that queries on these massive graphs are executed in real time is crucial for effective fraud detection. You can't wait house for a result when you need to stop a fraud transaction Now. The solution here is to employ hybrid architectures. The SA involves a combination of in-memory process keeping the most frequently access data in the computer's main memory for very fast retrieval, distributed storage, spreading the graph data across multiple missions to handle the sheer volume on to allow the parallel process. Financial data is highly sensitive, and we have strict regulations like GDPR and CCPA to add high two. We need to able to analyze the data. To detect fraud, but we also need to protect customer privacy. The solution to this involving implementing granular access controls, defining very specific permissions for who we access, what data within the graph. The solution to this involves implementing granular access controls, defining very specific permissions for who can access what data within the graph, data anonymization techniques, masking or removing personally identifiable information where possible while still preventing the structure of the data for analysis. To effectively address the challenges and successfully implement graph databases. Here are some key recommendations for financial organizations. Begin by identifying specific use cases where graph approaches often the most value. Don't try to implement graph databases everywhere at once. Focus on areas where they have to be biggest impact, such as money laundering, detection. Synthetic identity fraud are complex transaction fraud schemes. Starting with a clear focus will help you to define your requirements and measures your success, and start with pilot implementations. Begin with smaller projects to test the technology, learn best practices, and build internal expertise. It is crucial to invest not only the necessary technical infrastructure, but also in comprehensive analyst training. Your third analyst need to learn how to effectively query and interpret the graph data. The third one, ISPE Center of Excellence. This can be dedicated team at a cross-functional group responsible for establishing best practices, sharing knowledge, and providing support for graph databases, initiatives across the organization. It is also essential to establish robust governance. Frameworks to ensure data quality, security, and compliance. Very importantly, integrated graph solutions with existing systems. Graph databases are not intended to replace all your existing fraud detection tools. Instead, they should be integrated to complement or enhance your current capabilities, creating a more comprehensive and layer defense against fraud. Let's wrap up by looking ahead and summarizing both the feature trends in graph based fraud detection and my key recommendations for financial organizations. The feature of graph based fraud detection is being shaped by several key trends in time processing capabilities. The ability to detect and respond to fraud activity as it happens. Is becoming increasingly crucial. This means moving beyond batch processing of historical data to streaming analytics that can analyze transactions in milliseconds. Feature systems will leverage advantages in-memory computing, distributed processing, and a to provide instantaneous fraud alerts and prevention. Federated learning across institutions. This is very promising area that addresses the challenge of data privacy. Federal learning allows multiple financial institutions to collaboratively train fraud detection models without actually sharing their sense to customer data. Instead, models are trained locally at each institution and only model updates are shared. Preserving privacy while improving the overall effectiveness to fraud detection, improving explainability of complex models. A fraud detection system become more sophisticated, often employing complex AI techniques like graph neural networks. It's essential that these models can explain their decisions. Detecting sophisticated fraud schemes that involve multiple coordinated transactions and accounts. Focusing on these high impact areas will maximize the on. Investment and demonstration the value of graph technology. With pilot implementations, do attempt your large scale enterprise wide deployments from outset Instead, begin with a smaller, well-defined pilot project to test the technology and learn the best practices and build internal expertise. This approach allows adjustments and minimizes the risk. Crucially, invest in infrastructure and analyst training. Graph databases require specialized infrastructure. For fraud, analyst need to be trained on how they effectively query, visualize, and interpret the graph data. By following these recommendations, financial organizations can successfully adopt graph databases and position themselves to effectively combat on evolving threat of financial fraud.