The 5 Steps You Must Follow to Make AI Efficient
Written by Imam Hoque
Published: 11th Sep 2019
Whether you like it or not, artificial intelligence (AI) is here to stay. With almost every technology company saying”we use AI” and”we can make you 1 million per cent more effective,” businesses are not only interested in how to use AI, but are under internal pressure to make it work.
So why are businesses so often underwhelmed by the results of AI?
Challenges of AI
AI comes with 3 main challenges. Firstly, adoption; getting people to move away from the traditional methodologies they know and have long relied on is quite difficult. Secondly, many current AI methodologies are ‘blackbox,’ meaning users can’t see how or why the outcomes are delivered, and consequently don’t understand or trust the results. Finally, automated systems can deliver false positives when poorly tuned and applied, which can decrease efficiency rather than improve it.
At the same time, many people who bring in these technologies often don’t realise that even AI needs a helping hand. Simply throwing compute capacity at the problem is akin to the monkeys and typewriters theorem.
How to use AI well
Given these challenges, many businesses are left wondering what they can do to help their AI truly meet the promise of 100% effectiveness? While there’s no magical silver bullet, to get the best results, start at the foundation – the data. If you want results that match the patterns in the real world, and that help you solve real world problems, your starting point has to be the underlying data that represents the real world. This is something called network analytics.
It sounds obvious, but as anyone who’s worked with data or AI will know, it’s not simple. However, this 5-point recipe can start you on the journey to getting real world results from your AI, and move you toward becoming a data driven organisation:
1) Resolve your entities: Match entities, create a single customer view
Data pertaining to individuals, organisations or real-world concepts should be joined together in a process called Entity Resolution. An example of this is creating statistical single view of customer. However, match rates are often as poor as 50% (on a good day!) due to data quality issues, or are confused by trying to match complex entities such as businesses. Fortunately, there are techniques that can raise resolution rates to 85%+ by using multiple data points to overcome data quality challenges.
2) Create networks: Link the networks together, uncover the relationships
Entities interact in the real world and form networks: businesses have directors, customers, suppliers and employees. People have networks too – those they live with, places they visit together or others they interact with. However, to consume these networks using AI, you need to generate statistics about the networks, implying that networks do not go on forever. This in turn means they have to be”bounded,” with the question then becoming where to make the edges of a network. Furthermore, you have to deal with over-connectivity (e.g. many people pay utility bills, but are they all be connected? Probably not). But once you have good quality networks in your data, you’ll never look back.
3) Apply network analytics: Understand your network, analyse the relationships
Now you are ready to develop your AI models, whether for detecting financial crime, assessing risk or identifying contagious churners. You’ll find that network analytics not only predicts 100% more of what you are looking for, but false positive rates plummet. The outcome can be up to a 3-fold increase in effectiveness by only focusing on those alerts of interest.
4) Operationalise analytics: Bring AI into the process to produce compelling outcomes
Often crucially missed is the impact that can be achieved by using network analytics to operationalise AI to produce compelling outcomes. Imagine the traditional AI model detecting insurance claims fraud: “this claim has 67% risk of fraud” (based purely on the claim data alone) – where does the investigator start? How long will it take them to investigate? Will they remember when the system was right or each time it was wrong? False positives can be the scourge of AI solution adoption. Now imagine the network analytics equivalent:”this claim may be fraudulent because:
- The claimant’s partner was previously insured and made a similar claim;
- The credit card that bought this policy, bought two other policies both having made similar claims in the last 3 months;
- The ratio of whiplashes to vehicle damage claims across this network is 3:1;
- The network has a 150% growth in claims over the previous month;
- The vehicle was involved with another vehicle with claims that were rejected for fraud.”
Clearly, an investigator can make a much faster decision when presented with an alert that has been elaborated using network analytics. This dramatically improves the throughput of the investigation team, often cutting effort by 70%. A similar approach can be used for AML investigations or tax compliance issues, as well as assessing the next product to cross sell to a business.
5) Corporate memory: Blend humans with AI, enhance effectiveness
Blending human decision making with AI is often under looked and is a missed opportunity, as it can significantly enhance future effectiveness. Every time a human makes a decision, that outcome can be stored against the appropriate entity within the network. As all these nuggets of insight are on a network, their impact is amplified as they influence the scoring of other surrounding entities on the network. For example, the average over indebtedness or payments in arrears on a network could be a useful variable to use in your models.
If you really want to up your game on data driven decisions, the next generation approach is to use network analytics. The benefits are significant, and once you have entities and networks in your data lake, your data scientists will wonder how they ever managed to deliver outcomes without them. You should aim to make this a fundamental utility within the data lake that can operate both in batch and real time. Technology is now available to accelerate your journey towards network analytics without having to re-invent the wheel. Your data scientists can deliver game changing results across your organisation using AI, by combining it with network analytics.
You may be interested in…
Creating Value For The Enterprise Using Data
In this episode, Vishal Marria, CEO at Quantexa, speaks with the Chief Data Scientist at Dun & Bradstreet, on overcoming common data challenges, digital resilience, and creating enterprise value using in AI and data & analytics.
How Danske Bank Is Adopting Data and Analytics Technology
To maximize the value of data, enterprises need the right IT infrastructure in place. In this episode, Bo Svejstrup, CIO at Danske Bank discusses resolving legacy data challenges, improving collaboration between business and IT, and the future of cloud adoption.
How Allianz Is Transforming Using Tech
Quantexa speaks with Allianz CEO to discuss the challenges of adopting technology across the enterprise, the role of data in customer-centricity, and leading transformation in the insurance industry.
New Risk Factor Guidelines to Strengthen Financial Crime Detection
The updated European money laundering and terrorist financing risk factor guidelines highlight taking into account “wider, contextual factors.” Find out how contextual decision intelligence can ensure enhanced risk detection and due diligence measures.
QuanCon 2021: Meaningful Data for Trusted Decisions
QuanCon 2021 Virtual explored compelling thought leadership from the Altimeter Group and Accenture, knockout presentations from State Street and ABN AMRO, and an in-depth show and tell on Quantexa’s new capabilities.
Tech For Good: How Standard Chartered Bank Is Revolutionizing Investigations
Learn how Standard Chartered Bank has made huge strides in harnessing the power of data to revolutionize financial crime investigations.
Reduce the tax gap, identify fraud and non-compliance, and operate as efficiently as possible with limited resources.
Reveal hidden risks and detect criminal activity faster. Reduce false positives to manage the cost of compliance. And improve investigations to make faster and more consistent decisions at scale.
Customs Agencies & Border Control
Contextual Decision Intelligence enables faster decisions, increased revenue collection and enhanced compliance. The Quantexa platform enables Customs and Border agency teams to analyze data successfully, automate and accelerate decision-making, and achieve improved results.
Identify potentially fraudulent activity by looking at people or transactions in isolation. Understand the context surrounding the organizations you do business with to make fast, accurate decisions.
Fraud, Waste & Abuse
Empower your team with the best tools available for today’s challenges to identify and prevent fraud, waste and abuse with contextual decision intelligence software.
Understand your customers, their business structures and supply chains. Make better lending decisions, faster. And support digital risk transformation.
Know Your Customer
Reduce significant manual effort across onboarding, refreshes and remediation. Automate checks, implement continuous monitoring, and focus on contextual decision making.
Generate a complete view of the context around your customers and prospects to build better relationships, reduce attrition and find hidden opportunities.
Master Data Management
Connect all data—internal and third party—to create a joined-up, contextual view of all the relationships between your customers and every other domain.
See how we help to reduce costs and improve coverage for financial crime compliance.
See how our platform uses contextual analysis to turn data into a high value asset.
See how our platform uses financial crime technology to enhance your existing IT ecosystem.