Transforming Data into Actionable Intelligence in Regulated Industries
We recently sat down with Cal Al-Dhubaib, CEO of Pandata, to chat about a range of issues related to artificial intelligence, machine learning, and data science. This post, which has been adapted from a previously published article, takes a deeper dive into some of the issues we discussed in the first part of our conversation.
In the first of a series of fireside chats between Bo Howell, CEO of Joot and owner of FinTech Law, and Cal Al-Dhubaib, CEO of Pandata, the two discuss managing data and regulatory compliance. Between the rich experience of Joot’s automated SEC compliance solutions and Pandata’s design of human-centered, trusted artificial intelligence in healthcare and other heavily regulated industries, the conversation was compelling.
Let’s face it: in the business world today, one thing most companies have in common is a lot of data. But having a superpower and controlling it are two different things. The same goes for data. As aptly put by Cal Al-Dhubaib, “It's not about how much data you can track anymore, but what data can you measure?”
Organizations need to know how to use their data to comply with industry-specific regulations but also more universal regulations like CCPA and GDPR. Having data is definitely a superpower that can create a competitive advantage, increase revenue, and even streamline processes. But data needs to be wielded for good, and that’s where trusted AI comes in—making fairness, transparency, and privacy a primary focus.
Many businesses are struggling with questions like these:
- Where do we start with building a machine learning solution?
- What should the solution be?
- What value will an AI-based solution add to the business?
Machine learning brings three buckets of value-adds for businesses:
- Streamlining tasks
- Improving efficiency and accuracy
- Augmenting human intelligence
Let’s explain this with an example of a marketing team looking for a prospect. The process of finding a potential lead, checking their LinkedIn profile, documenting each data point, and analyzing it to qualify a lead is a massive undertaking for even an entire team. This is where machine learning can help. Machine learning algorithms can streamline tasks, track every detail, and help you with recommendations that otherwise would have taken ages to accomplish.
Improving Efficiency and Accuracy
Repetitive tasks are a time sink. Some of these tasks may require more in-depth analysis and higher efforts to generate results. As humans, we can only process so much information and do so much in a day.
Take the example of a call center where QAs randomly select a few samples to evaluate the quality of customer service. Such quality testing only scratches the surface, and many significant details can go unnoticed. Bo Howell explains: “In financial services, SEC registrants need to comply with lots of rules and regulations. One required task is email review. The amount of email produced by even a small business is huge, and no one can review it all. So people tend to do it monthly or quarterly, just taking a sample. At best, they’re getting a snapshot and hoping to get lucky. Trading is another example. There can be thousands of trades a day, maybe more, and monitoring all of them is overwhelming. Some larger businesses will hire someone to do that as a full-time job, but even that person has capacity limits.”
When processes like these use ML algorithms trained with a range of data sets, sampling approaches, and scenarios, the accuracy of each sample can improve with automation. With such algorithms, businesses can expand sampling activities, freeing people to focus on more fulfilling work.
Augmenting Human Intelligence
Humans are limited when it comes to monitoring anything and everything. The cybersecurity space is one such example. For humans, finding an anomaly or a malicious pattern among millions of data transactions or exchanges is almost impossible.
This is why human-in-the-loop AI matters. Designing with humans as a part of the process will help the AI better automate, track, monitor, and detect malicious patterns in the cybersecurity space, more like a human would. Business owners, managers, and employees should consider how AI can supplement their work, not replace it. Additionally, when designing an AI product or process, consider the human-user interface. If you want people to engage with a machine, it should feel natural.
Every business should be looking to use data to its advantage. However, it's not just the data that’s valuable but also the analysis, context, and execution of intelligence derived from such data. Starting with a trustworthy AI strategy can bring structure to the chaos and enhance business intelligence, especially in regulated industries where concerns around privacy are paramount.