Human-centered AI: AI built for Humans, not built to replace them.

With some of the leading minds in AI constantly urging caution, it is easy to be confused by the noise around this nascent technology. Generative AI burst on the scene and changed everything just as Finance firms struggled with long-term implications of digital transformation, forcing them to react and adapt once again. But at this point, artificial intelligence is fundamentally disrupting the financial services sector. While generally making us more efficient at some tasks, the application of AI also raises crucial challenges. The most important among these is how to embrace AI in ways that are beneficial to humans, instead of being competitive with them and eventually replacing human jobs with machines.

As demonstrated by increased spending on AI training data and large language models, the financial industry is generally bullish on AI. Business leaders understand its power to increase revenue and reduce expenses. However, there’s also growing interest in how to make the technology more in tune with our values and more complementary to our workflows. That’s where human-centered AI comes in.

Human-centered AI explained

Human-centered AI (HCAI) is designed to create systems that augment and amplify instead of competing with human abilities. These systems are meant to be understandable, transparent, and easily explainable. While keeping people in control, they work to ensure that AI tools meet the needs of human beings and deliver incremental value-added outcomes while seamlessly integrating into the person’s workflow.

In the HCAI framework, what’s at the forefront are the values, needs, and goals of human beings. As opposed to traditional AI which prioritizes optimization and efficiency, HCAI strives to create systems that value individuals and improve their quality of life and work.

The framework recognizes the strengths of humans and leaves those parts of the job to them while letting machines handle tasks that are onerous, repetitive, time-consuming, or error-prone.

Examples of how to use human-centered AI to help humans

At its core, human-centered AI adds empathy and respect to systems. Instead of merely focusing on productivity and growth metrics, HCAI delivers holistic benefits to the individuals concerned. 

ModuleQ was founded on the vision to bring human-centered AI augmentation for business professionals to market. Our patented Personal Data Fusion technology was invented to identify business professionals’ customer-oriented priorities automatically from their digital work traces, and a knowledge-aware information recommender system to recommend information to professionals within their workflow.

Let's consider a few examples:

1. Build stronger account relationships

Building a relationship with an account takes time and energy. Finding relevant data and content that will resonate with who you are talking to is challenging but it is the best way to win. According to a BCG article, What Does Personalization in Banking Really Mean, the potential of personalization lies in transforming all of an organization’s customer interactions by using data and analytics to anticipate individual needs, target individual segments, and build deep relationships that stand the test of time. Personalization is not primarily about selling, it’s about providing service, information, and advice, often on a daily basis or even several times a day.

According to recent research, superior knowledge about their market—customers, users, competitors, and other market entities—can help organizations create a sustainable competitive advantage. HCAI can bring that competitive advantage with relevant data, and crucial insights that can enable personalization. By proactively surfacing relevant information and learning each banker’s interests and preferences from their feedback and behavior, HCAI can reduce the time bankers spend searching for information across multiple siloed systems.

Source: Collaborative intelligence: How human and artificial intelligence create value along the B2B sales funnel

2. Boost performance and work-life balance

Financial service professionals often have long hours, demanding workloads, a competitive work environment, and a steep learning curve. Investment bankers especially are known for their long hours and lack of work-life balance which can lead to burnout and high employee turnover. According to Buyside Hustle, investment banking roles rank the worst in terms of work-life balance with mega fund private equity roles coming in second worst. 

AI has the potential to revolutionize work-life balance, especially in demanding and complex roles such as investment banking. By automating tasks, personalizing workflow experience, and providing real-time feedback and support, AI can help financial professionals feel more confident and supported in their role. 

A Harvard Business School Study Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality states: Across 18 realistic business tasks, AI significantly increased performance and quality for every model specification, increasing speed by more than 25%, performance as rated by humans by more than 40%, and task completion by more than 12%. Further, it operated in a way that benefitted bottom-half performers the most, though all users benefitted from AI. Thus, AI seems to both level performance differences across levels of ability and raise the quality of work for inside-the-frontier tasks. These findings suggest a need to understand how work can be organized to better integrate AI.

3. Insight delivery to reduce information overload

Financial professionals are overwhelmed but the number of tools/portals, data sources, and communication channels. Information is everywhere but finding relevant and meaningful information is overwhelming. According to a Harvard Business Review article, Reducing Information Overload in your Organization; information burden is defined as a set of information that is:

• Duplicative: 57% of employees and managers say they often receive multiple communications about the same or similar topics at the same time.

• Irrelevant: 47% say that the company communications they receive are unrelated to their day-to-day responsibilities.

• Effort Intensive: 38% say they have to do extra work to keep up with the amount of information they receive at their organization.

• Inconsistent: 33% say that the company communications they receive are often inconsistent or internally conflicting.

ModuleQ's People-Facing AI uses HCAI principles to reduce the burden of sifting through massive amounts of information. By connecting your work data and enterprise systems information, ModuleQ surfaces insights at timely moments in a curated feed. Reducing information overload with AI that sends you timely, relevant insights that help you:

• Reach out to accounts at the right moment with personalized content.

• Come to every meeting prepared with the latest internal information from CRM, internal and external (third-party) data sources

• Get out of endless loop of email and stay in your workflow in Microsoft Teams
  
  

Why a human-centered approach to AI is a necessity

If you’re in the financial service industry, you should consider a human-centered approach to AI for the following reasons:

To make AI more representative: A human-centered approach to AI will take into account the diversity of the population for more representative data.

To build trust with all stakeholders: By addressing their security and privacy needs - and being transparent about your actions - human-centered AI can help the industry build trust with customers and regulatory authorities.

To provide human-centered experiences: HCAI can analyze an individual’s actions and interests and combine them with human intervention for more customer-centric experiences. A robo-advisor with human oversight is an example of this.

Conclusion

With the evolution and widespread application of its technologies, the financial sector will benefit more if it uses a human-centered approach to AI. It will make the whole system more trustworthy, empathetic, responsible, and reliable.