The Center for Data Innovation spoke with Imam Hoque, chief operating officer and chief procurement officer of Quantexa, a UK-based company that helps organizations better use their data for operational decisions. Hoque discussed how using AI can help organizations discover risk and insights more effectively from large volumes of data than traditional methods.
This interview has been edited.
Christophe Carugati: What is the most pressing challenge your clients face when trying to make use of their data?
Imam Hoque: Organizations are drowning in a sea of data. They struggle to piece together the information they have into a holistic picture which they can use to make business decisions. This means they miss risks and opportunities because they cannot see a full picture. One such risk, for example, is in the banking sector.
Given the amount of data banks collect, technology can make an exponential difference in anti-money laundering risk mitigation. Money launderers develop enterprise-level and intricate criminal webs of companies using sophisticated methods such as trade finance and capital markets to conduct their activity, hiding dirty money behind these obscure, seemingly legitimate, functions.
Legacy anti-money laundering tools rely on a rules-based system that flags each risk in isolation, depending on a strict set of parameters. The problem is that this makes them rigid—criminals can slip through the net easily, and decisionmakers miss the full picture. This approach also creates a staggering number of false positives—95 percent on average.
Rather than focusing on the traditional detection of financial crime through individual transactions, banks need to be looking at how these complex criminal networks operate to see who and what is hiding behind them. “Contextual decision intelligence,” a new approach to data analytics that combines data context with AI, pioneered by Quantexa, screens both internal and external data, providing insight into these criminal networks by shedding light on how groups of transactions relate to criminal businesses and highlight the people behind them.
Carugati: How do you help organizations to get more insights from data?
Hoque: Ultimately, to make better use of the data in real life in order to detect risk and make better decisions, the meaning or “context” behind the data is what truly matters. Context provides a broader understanding of events, people, or items, framing knowledge in a larger picture and giving it perspective. Without being looked at in its context, a data point is simply a standalone number.
Human teams do not have the capacity to gather this context manually, leading to missed insights on existing or future risks and opportunities. But combined with cutting-edge AI capabilities, where algorithms allow for faster detection of activity and fewer false positives, both time and money are saved.
This method of combining data context with powerful artificial intelligence capabilities is known as contextual decision intelligence, or CDI. It is achieved through three key steps: entity resolution, network generation, and advanced analytics framework. First, entity resolution is the process of working out whether multiple records are referencing the same real-world thing, such as a person, organization, address, phone number, bank account, or device. Entity resolution takes multiple, disparate data points—from external and internal sources—and resolves them into a single, unique entity. Second, network generation creates a dynamic view of the bigger picture by automatically compiling the most relevant connections, entities, and data for a specific decision. Third, the advanced analytics framework is the simplest way to use the context of these resolved entities and relationships in scenarios, rules, and models. Quantexa is the spearhead of CDI, using data analytics to enhance organizations’ existing analytics approach with contextual capabilities that bring data together from any source.
Carugati: How does your platform help clients improve and scale their decision-making?
Hoque: Making good operational and strategic decisions based on trusted and connected data is key for data-intensive companies of the 21st century. This is particularly critical for financial institutions, with the increasing amount of regulatory pressure put on banks to prevent financial crime.
To succeed at tackling evolving types of financial crimes, organizations need an approach that collates all available data sources to derive an accurate picture of people and their connections to organizations, things, and places. This helps decisionmakers ascertain whether something looks suspicious in significantly less time, with more accuracy and less human effort.
One example of how this works in real life is the world of fraud. Since the pandemic, fraud has increased at a staggering rate, especially with the pandemic finding businesses vulnerable. Organizations will often look at activity, behaviors, companies, and individuals in isolation, preventing them from seeing the bigger picture needed to detect fraud. Quantexa empowers banks to configure their systems to more effectively distinguish between legitimate and fraudulent behaviors using fraud detection software which relies on the context found by connecting all the data at hand, internal and external, to surface suspicious patterns.
Carugati: Which challenges are companies facing with the increase in data volume?
Hoque: Organizations have an abundance of data, but it is blocked from being truly useful as it is fragmented and siloed. Without connecting data to entities of interest, a data point lacks value. Again, using the example of financial crime, anyone can set up a bank account, but if that person’s previous history, current connections, and business activity are high risk, then it is worth investigating.
By connecting these large, underused, and often disparate datasets, organizations can unlock context and gain insight into the connections, relationships, and behaviors the data represents. Bringing into the mix of external data sets, such as aggregated corporate registries, law enforcement watchlists, or known criminal network members, decision models can begin to piece the puzzle together.
This provides a broader understanding of entities—be it events, people, and companies—framing the knowledge and giving it perspective. Having full oversight of this context means investigators can use their time more effectively, focusing instead on genuinely risky transactions.
Carugati: What are the opportunities and benefits of automating data analytics?
Automation is the key to effectively analyzing big data. Automated data analytics through AI and machine learning saves an enterprise time and money. Employee time is more expensive than computing resources when it comes to data analysis, and machines can perform analytics efficiently.
AI and machine learning are being adopted more widely across businesses large and small. Businesses are increasingly using AI-enabled services in the cloud, allowing them to enhance their product capabilities, better interact with customers, streamline business operations and create precise business strategies. AI improves operational efficiency and accuracy through automating all tasks which are repetitive or require intensive manpower. This will allow banks to shift resources and focus on more value-generating activities.