Knowledge Graph technology offers value to many industries – but at this first stage of our journey, our primary focus is on the finance sector. On this page, we outline just a few of the numerous use cases we plan to address over the months to come.
Many of these problems are notoriously hard to tackle using traditional database technology. The distinctive architecture of an Enterprise Knowledge Graph draws together data from disparate siloed data sources – then finds connections between them, unlocking new approaches to these previously intractable use cases. Read more about our vision for the Enterprise Knowledge Graph.
Client 360 / KYC
Every large enterprise struggles to achieve a comprehensive understanding of their customers – especially when relationships span multiple lines of business or geographies. The holistic view offered by an Enterprise Knowledge Graph is all but perfect for this use case, seamlessly linking information from diverse source systems to build a fullest possible picture of each client.
Anti Money Laundering (AML)
AML is an immense task for any bank, requiring ongoing analysis of individual transactions, parties, and the relationships between them. Traditional databases cannot natively reflect this complex and ever-changing picture, requiring complex code and detailed business knowledge to form connections and understand meaning. An Enterprise Knowledge Graph is designed from the ground up to join dots and identify discrepancies, while also delivering an enhanced understanding of client behaviour to reduce the level of false alerts.
Though stress testing is certainly not a new obligation for banks, recent regulatory changes have dramatically increased its complexity. New demands on the scope, granularity and consistency of results require consolidation and aggregation of data from across the organization. An Enterprise Knowledge Graph allows stress testing to be performed on a single, integrated and reconciled data source, delivering results that are consistent and repeatable.
Fundamental Review of the Trading Book (FRTB)
The complexity of FRTB models presents a particular challenge, requiring the lineage of risk factors to be traced back to their original authoritative data sources. An Enterprise Knowledge Graph tracks both interdependence between data, and a traceable, multi-tiered provenance for each datapoint. Combined with powerful analytical tools, there is an opportunity for an FRTB solution to move beyond a regulatory requirement, and become an indispensable tool for capital optimisation and planning.
Conduct risk, such as unauthorized trading, remains a major issue in the financial industry. Similarly to KYC and AML, most organizations currently rely on rules-based techniques to generate control reports – yet high levels of false positives make it difficult to identify genuine concerns. In contrast, an Enterprise Knowledge Graph offers a connected, holistic view of an employee’s activity across the whole organization, while also clearly revealing any inconsistent or irreconcilable data.
Enterprise IT Inventory
The IT assets of most enterprises are immensely complex and ever-changing. In a traditional siloed environment, it is very difficult to understand interdependencies between systems. The connected design of an Enterprise Knowledge Graph can map this entire landscape, enabling analysis of resilience scenarios and allowing the impact of planned changes to be better understood.
Enterprise Entitlement Management
The fractured, fast-changing landscape of a complex organization makes it all but impossible to implement frictionless entitlement management. Typically, entitlements are granted through a manual approval process, which costs resources and inhibits staff productivity. The holistic, responsive view offered by an Enterprise Knowledge Graph offers an alternative possibility, where entitlements are managed automatically based on data such as policy and roles.
In the longer term, when an Enterprise Knowledge Graph is mature and established, it has significant potential to prevent and detect cyber-attacks. By building a fully-connected view of an organization’s assets and supply chains assists, the Knowledge Graph assists with vulnerability analysis – while real-time analysis of activity across the whole enterprise may identify attacks as they occur. Dynamic, automatic entitlement management also closes potential attack vectors and increases the security of the enterprise as a whole.
Large and complex enterprises can be difficult to navigate – for staff and, sometimes, even for management. The core design of an Enterprise Knowledge Graph reflects diverse and disparate relationships, allowing concepts such as people, locations and legal entitles to be represented natively and intuitively. Applications include reporting – with aggregations occurring at the right layers and consistently over time – and improved ownership, accountability and transparency.
Mergers & Acquisitions
Any merger or acquisition inevitably creates technical debt, with maintenance and integration of legacy systems often generating considerable costs over a number of years. The open, highly interoperable architecture of an Enterprise Knowledge Graph offers a practical and elegant route to integration – allowing systems to be opened up to each other with a minimum of disturbance, and then, over time, gradually migrated or retired.
These are just a few of the potential use cases we are exploring at agnos.ai. If you have your own suggestions, or if you’d like to discuss how an Enterprise Knowledge Graph can deliver value for your business, get in touch with us today.