Business
ALLIANZ BENELUX IS USING GRAPH TECHNOLOGY TO BEAT FRAUD AND BOOST CUSTOMER-CENTRICITY
Published
2 years agoon
By
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Amy Hodler, Director, Analytics and AI Program Manager at Neo4j.
Data expert Amy Hodler examines how graph technology is reducing insurance fraud and providing customer insight at one of the world’s largest financial services companies
Financial services firms constantly have to fight financial criminals, but it is getting more demanding for organisations to identify and stop fraudulent activity at the scale it now occurs.
Traditional methods for monitoring fraud, such as setting up rules to examine deviations from normal purchasing patterns, use discrete data. This is useful for catching individual criminals acting alone, but this approach falls short when it comes to detecting fraud rings. Sophisticated criminals continuously alter their strategies to circumvent detection. They utilise synthetic accounts to carry out what appear to be unrelated activities by unconnected individuals. However, these activities are in fact well-coordinated and criminally linked.
The financial services sector needs a better way to follow the trail from one account to another to determine how activities that on the surface appear unrelated are in fact connected. This requires having a 360-degree view of the intricate fraud network to determine how suspicious events are linked.
Fraud detection with graphs
Graph database technology may be an invaluable tool in fighting fraud. In contrast to traditional relational databases, graphs not only interpret individual items of data, but also their relationships with one another. An increasing number of the world’s leading financial institutions are using graph databases to model and monitor data about customers, accounts, devices, locations and other attributes to identify fraudulent activity. Allianz, a multinational financial services company offering insurance products and services to 100 million customers in more than 70 countries, is one such.
As a truly customer-centric insurer, Allianz Benelux takes a zero-tolerance stance on fraud. As the subsidiary’s chief data and analytics officer, Sudaman Thoppan Mohanchandralal, explains, “We need to secure customers from risk – not just today, but into the future. We can only do that by having full insight into the risk environment and with an ability to predict it for our customers.”
Relational data model problems
Mohanchandralal’s colleague, Dr. Jan Doumen, strategic lead for Customer & Broker Information and Insights, agrees. “The best way to understand your customers and the risks they are exposed to on a daily basis is by storing, analysing and visualising them through connected data.
“Graph technology does this at scale, which means we no longer have to rely only on highly demanding, traditional relational technologies.”
Historically, building internal visualisations of suspicious behaviours with relational technology had been too demanding, Doumen confirms. The latest fraud countermeasures, such as network tracking, were too complex to build in a relational database. Sudaman calls this process a ‘2 by 2’ approach, where SQL database-style tables with rows and columns don’t offer the data connections fraud detection and prevention requires.
Working with a relational data model doesn’t allow the Allianz Benelux team to extract useful data on the fly. In contrast, graph technologies spot potentially fraudulent activity in Allianz Benelux’s ecosystem by disclosing concealed illicit connections. Bringing all the customer data into a graph database permits the Allianz Benelux anti-fraud team to reveal the risk exposures in a motor or household context.
“It is the combination of multi-node, multi-connection snapshots of customers and the much more efficient search possibilities coming from graph technology that we believed would revolutionise the way our internal business handles customers’ risks,” Doumen confirms.
Clear business benefit
Equally important for the Allianz Benelux team is having a 360-degree view of the customer. The Belelux operation has gone through a series of mergers and acquisitions and its customer data has become dispersed in separate silos, which has led to a number of operational inefficiencies.
“When we were able to get to a level with graphs to show colleagues this holistic view of a customer, it was so much easier for them to understand rather than through a table with rows and columns. This will enable them to personalise their services towards our customers,” Doumen adds.
Allianz Benelux’s success using the native graph approach has resulted in clear business benefits. Over the course of two years, €2 million of operational profit value was identified. Given the advantages realised with graphs, the Allianz Benelux team plans on offering the solution to other parts of the organisation.
Graph databases can future-proof an organisation’s fraud prevention initiatives by providing insight based on data relationships and connected intelligence. They can also unlock data silos and generate a more unified view of customers – helping you achieve full ‘customer-centricity’, as well as drive more revenue. Sounds well worth investigating.
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Business
Does the middle market have a financial edge?
Published
22 hours agoon
March 22, 2023By
editorial
Companies tend to look up the ladder when searching for ways to improve efficiency and business performance. What are larger competitors, or others outside their industry, doing right that they can learn from and implement?
What smart technologies or bright ideas do they have that could create efficiencies for them, too?
As we enter yet another likely volatile year for business, punctuated by recession, should businesses continue to only look up? And could the approach of a slightly smaller business offer more of a competitive edge?
Large corporates tend to pioneer innovation in automation by simple virtue of the resources they have. Home to transformation directors and departments, with the ability to implement large overarching software systems, they pave the way for others and are often the first to digitise their source-to-pay cycle at pace.
While growing businesses understand the merits of full automation, implementing it is often too expensive and it doesn’t bring the rapid realisation of benefits that they need. They need to consider what will bring them the biggest return on investment – and the reality is that those in the middle market don’t necessarily need all the elements of an ‘all-doing’ piece of software. What’s more, without dedicated personnel to project manage a transition, they frequently lack the currency of time to be able to comfortably transform working practices, and take staff with them on the journey, without taking resource from other areas of the business.
For SMEs, digital transformation has never been quite as seismic a shift. Instead, they tend to take a modular approach, employing digital solutions only for particular areas of their finance department, where they need them. This has never been a particularly strategic move. Rather, for a growing business that values quick results and watches their outgoings with greater scrutiny than their larger counterparts, it’s something that suits them better. A modular approach also comes with very little disruption and can be implemented relatively seamlessly into their existing organisational setups.
But while growing businesses are opting for a modular approach because it’s the most cost and time effective option for them, the benefits go far beyond that. The beauty of a modular approach is that it is agile. The last three years – with pandemics, an increasingly challenging climate and shifting geopolitical tensions impacting our global economy – have only served to remind us of how suddenly, and drastically, a business landscape can change. The companies that have weathered the storm are those that have reacted and adapted quickly – those that have been capable of changing the way they do things with little impact on day-to-day operations. A modular approach can offer just that.
Businesses using modular finance technology can integrate small solutions that sync up with the rest of their processes, quickly and seamlessly – and these systems can be integrated into their existing Enterprise Resource Planning (ERP), too. There’s no restriction of a monolithic or aging piece of software either – finance teams can add and update small solutions to their daily operations without the upheaval of having to replace or update large IT infrastructures or wider working practices within the business to accommodate the new software.
Unrestricted by entrenched and hard-to-change systems, the speed with which SMEs are able to react to market changes is miles ahead. A prompt software add-on to manage risk, or create a quick fix in response to a market shift, can be virtually a knee-jerk reaction. SME’s abilities to bend and flex to today’s world efficiently is seeing them reap the benefits of a modular approach. It’s lean, it’s fast and it’s facilitating their growth with a strong competitive edge. And as some of these companies’ growth propels them into the large corporate sphere, they’re choosing to keep a modular approach to finance. It will certainly be interesting to watch those middle-sized companies which grow to the extent that they find themselves competing in the same space. With no financial remodelling to assume a large ‘all-doing’ piece of software, they’ll be competing against their counterparts with completely different tools in their arsenal.
With technology, working life and business needs continuing to change day to day, we have another year ahead of us that will see companies running to keep pace with each other – and fast-growing companies’ approach to finance could be the silver bullet that enables them to catch up with, and even take on, big enterprises. It might just give them a competitive edge against large corporates in these turbulent times.
Business
Hybrid Intelligence – The only way to face the problems of the future
Published
2 days agoon
March 21, 2023By
editorial
Author: Prof. Dr. Iris Lorscheid, Vice-Rector Research and Professor of Digital Business and Data Science Computer Science at the University of Europe for Applied Sciences
Our world is complex and challenging, and the problems are likely to become more complex in the future. The world becomes more interconnected and globalized as technology advances, the global population grows, and resources become scarcer. All of this needs achievements in innovations in cybersecurity, sustainability, resource management and more. Hybrid Intelligence is the future because it combines the strengths of humans and machines to solve complex problems that neither humans nor machines can solve on their own.

Prof. Dr. Iris Lorscheid
The concept of “Hybrid Intelligence” was introduced by Dominik Dellermann to describe the collaboration between human intelligence and artificial intelligence (AI) in order to achieve more effective problem-solving and decision-making. The focus is on developing more advanced AI systems that can work with humans in the best possible way.
Together, human and AI can create solutions that neither could achieve alone. By combining the strengths of both, complex problems can be solved, and new insights can be gained faster, more successfully, and more comprehensively than by working individually.
Humans have long understood that collaboration is more effective than individual effort, which has led to our success. The success of a group depends not only on the best and brightest minds but also on effective teamwork and interaction between individuals. With AI as a new team member, the question now is how we can best strengthen each other and find new solutions together.
To ensure responsible and ethical use of AI, it is critical to discuss ethical considerations when working with it. It is important to ensure that AI systems are safe and reliable in order to prevent harm to people and society. AI systems may perpetuate existing social and cultural biases. Transparency in decision-making processes can aid in the development of trust and accountability for the outcomes produced by AI systems. Protecting personal data privacy is critical in order to protect individuals’ rights and autonomy. Establishing accountability for AI decisions entails ensuring a clear chain of responsibility for any negative outcomes. Addressing these concerns is critical for developing beneficial AI systems that can help individuals and society while minimizing potential harm.
AI should be viewed as a tool to assist humans rather than to replace them. Innovations are an opportunity for a better world, and a better life.
AI can help us understand climate patterns and predict weather conditions by analyzing large amounts of data from various sources such as weather satellites, sensors, and historical climate data.
AI can help farmers maximize crop yields while minimizing waste. Farmers, for example, can use AI-powered sensors to monitor soil moisture levels and plans. AI can provide farmers with the information they need to take preventative measures to save their crops from disease or to increase food production efficiency.
The analysis of complex medical images, patient histories and treatment results will help doctors around the world to come to better conclusions and decisions.
Concerns and fears are frequently associated with the introduction of new technologies such as AI. Overcoming these fears requires an open and informed debate focused on the benefits and potential of hybrid intelligence. By educating the public and encouraging open communication between developers, users, and authorities, these worries can be eased.
Change introduces a variety of challenges that require innovations. Innovations, in turn, cause further change. We need to be open for this reoccuring cycle to create new opportunities and to improve the quality of life for many.
AI advancement holds great promise for addressing some of the world’s most pressing issues. Let’s go on an adventure and investigate the possibilities of human-AI interaction in business, education, and our every-day life.
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