Partner and Head of Trusted Data and AI,
Artificial intelligence (AI) is no longer a futuristic theory — it’s pervasive throughout our daily existence, in our offices, our kitchens, and our cars. As its capacity and reach grows, so does concern about just how much we can trust the AI in our lives.
Earlier this year, IBM announced new capabilities for IBM Watson designed to help organizations build trustworthy AI, expanding on its ability to support businesses as they govern and explain AI-led decisions, increase insight accuracy, mitigate risks, and meet their privacy and compliance requirements.
I believe trusted data and AI is one of the ‘grand challenges’ of our generation. Developing and enforcing standards, ethics, and governance around the use and development of trustworthy AI systems has become an urgent priority – Pavel Abdur-Rahman, Partner & Head of Trusted Data & AI, IBM Canada
An IBM-commissioned survey found that trust, transparency, and explainability are top-of-mind concerns for businesses, with 84 percent of AI professionals surveyed agreeing that consumers are more likely to choose services from a company that offers transparency and an ethical framework on how its data and AI models are built, managed, and used. However, the barriers to developing trustworthy AI and mitigating risk remain pervasive, with 82 percent of AI professionals surveyed saying that their organization has been negatively impacted by problems, like bias, with data or AI models.
With sentiment as pervasive as this, there’s growing urgency for more rigorous standards for the ethics and governance around the use and development of trustworthy AI.
“AI is rapidly emerging as an important tool for solving humanity’s challenges, but it’s not without its risks,” says Pavel Abdur-Rahman, Partner and Head of Trusted Data and AI at IBM Canada. “People are increasingly uneasy about allowing intelligent and autonomous machines to access their personal data to make decisions. This has prompted a growing demand for ethically-aligned AI that creates trust and protects privacy, fends off would-be hackers, and prevents bias.” Abdur-Rahman goes on to cite a recently-unveiled proposal by the European Union (EU) as an example of this trend, with the EU calling for strict horizontal regulations to govern the use of AI, with severe penalties for those who contravene the rules.
Banking on Trusted Data and AI
In today’s competitive environment, financial services institutions are using data — including customer, firm, and third-party data — to deliver innovative alternatives to traditional banking solutions. With open and trusted data as the main currency, open platforms deliver relationship-based services built on customer engagement and experience, and in so doing, allow banks and the emerging fintech companies to leverage their complementary strengths, instead of competing.
Driven by trusted digital relationships between banks and customers, this new business model is powered by artificial intelligence (AI) and generates value even during a pandemic lockdown. However, many customers are rightly concerned about how and when their personal data is being used. Trust in AI systems will increase only when customers are confident that their data and insights are protected, not leveraged for profit without their permission.
Customers and investors will flock to the most trusted brands in AI, and financial institutions that take a leadership role will benefit the most in the platform economy. Consider:
- According to a recent IBM Institute for Business Value survey, more than 68 percent of consumers are willing to share personal information and data with their bank or other financial services institution. Almost 91 percent of individuals who share personal data with their bank trust it to protect their personal information and data to at least a moderate extent, second only to their own employer.
- AI hyper-personalization increases the likelihood of attracting, retaining, and delighting customers with a company’s product offerings, services, and 24/7 availability.
- Data and AI also provide new tools in the fight against fraud. When effectively used, these tools improve fraud detection, reduce credit losses, and enable know-your-customer regulatory checks.
- The future relationship between bank and customer is likely to be much deeper and much more interactive. But getting there from here requires radical transformation across business and operating models, as well as changes in the way resources, business processes, and technologies are assembled to create value.