This interview analysis is sponsored by MindBridge and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page.
In today’s financial landscape, the adoption of AI is proliferating, but this technological shift presents significant challenges for financial departments and workflows in every enterprise. A 2023 report by the Canadian Office of the Superintendent of Financial Institutions(OSFI) highlighted that financial institutions themselves are increasingly incorporating AI to optimize operations. Yet, many enterprises across industries need help with governance, data management, and ensuring explainability in their AI models.
Their guidelines are drawn from executive surveys like that of the Institute of International Finance, which noted in 2020 that only 35% of global financial institutions claimed to have established a centralized specialist team, such as anAI Governance Council, that helps align the organization with data-based goals, assessing use case value, prioritization, and approving new AI models.
The game of catch-up makes for an uneven landscape of adoption across enterprise departments. It complicates effective AI integration in the long term, making it difficult to ensure transparency, explainability, and data management. The Superintendent’s report also emphasizes that, while many institutions are working to build AI governance frameworks, these frameworks often require significant cultural shifts within the organizations as more departments begin to use AI.
However, understanding how AI delivers ROI is vital for decision-makers to scale these systems across the enterprise effectively. On a recent episode of the ‘AI in Business’ podcast, Matthias Steinberg of MindBridge highlights AI’s short- and long-term returns, particularly when focusing adoption in financial workflows first.
Matthias says that doing so ensures that deterministic, first-generation AI capabilities can embed themselves in predictable systems, effectively laying the groundwork for scaling up to new generative AI capabilities. In the process, Steinberg identifies key use cases that help businesses navigate a highly complex industry. All of this underlies the question: With AI being a transformative force, what’s the most effective approach for finance leaders to extract accurate, measurable, and long-term value?
In the following subsections, we break down what Steinberg sees as AI’s immediate and future impact on finance workflows across industries from his conversation on the podcast with Emerj Senior Editor Matthew DeMello. In the process, we’ll illustrate two actionable insights for how to achieve ROI from these technologies:
- Prioritizing high-impact, short-term wins with deterministic use cases: Laying the groundwork for further generative AI (GenAI) adoption by targeting high-frequency processes, typically in repetitive workflows and detecting anomalies in financial data, for training machine learning models as new automation systems are being adopted.
- Long-term AI scalability: Investing in adaptable AI frameworks ensures institutions can maintain competitiveness and ROI as data governance, explainability, and regulatory standards evolve.
Listen to the full episode below:
Listen to the full episode below:
Guest: Matthias Steinberg, Chief Strategy Officer at MindBridge
Expertise: Corporate Finances, Automation, Long-Term Strategic Planning
Brief Recognition: Matthias Steinberg is a leading voice in AI-driven financial transformation, serving as Chief Strategy Officer at MindBridge. His career spans leadership roles at top financial institutions, where he spearheaded the adoption of AI-driven solutions to enhance audit quality, streamline decision-making, and optimize business operations. Prior to joining MindBridge, Steinberg held executive positions at global firms such as EY and KPMG, where he led initiatives in financial auditing and risk management. His in-depth expertise has been instrumental in helping organizations leverage AI to solve complex financial challenges, making him a recognized thought leader in the field.
Delivering Quick Wins with Deterministic AI First
Steinberg opens the conversation by emphasizing that the ability to automate processes and streamline operations are becoming critical enterprise capabilities in financial workflows across industrial sectors. He mentions a “tension” between financial teams desire for repeatable results, and the more probablistic nature of generativeAI systems, now at the peak of their hype cycle.
Inherent in these trends is an escalating challenges corporate finance teams face in managing large amounts of data in a time-efficient manner. These market forces drive enterprises to adopt AI for the technology’s ability to process and analyze vast datasets quickly and with higher accuracy than manual processes provides organizations with immediate operational benefits.
Steinberg explains that, in the process, short-term ROI is often realized through automating routine accounting tasks such as invoice processing, financial reporting, and data reconciliation. These AI-driven efficiencies lead to cost savings and allow finance professionals to focus on strategic decision-making. He also underscores that industries adopting AI for fraud detection and anomaly identification are seeing rapid, tangible results within the first year.
By leveraging AI for anomaly detection, finance teams can identify suspicious transactions or irregularities faster than through traditional methods. According to Steinberg, this early detection mitigates potential fraud risks and prevents large-scale financial losses. According to a report from Cambridge University’s Data & Policy Journal, AI systems have enabled companies to respond to fraud across a growing digital payment ecosystem, detecting fraud attempts with increased sophistication.
Steinberg stresses that organizations should start by identifying processes that are manual and time-intensive, as these are the low-hanging fruits of AI automation. He advises that by focusing on processes that are high-volume and repetitive, organizations can see immediate benefits. He notes, “Quite often, companies have actually been guilty of not deploying this type of technology to the extent that they actually could for their benefit.”
Moreover, financial teams adopting AI in fraud detection workflows will mitigate risks earlier and more effectively. According to a report from Deloitte, AI-enabled fraud detection systems reduced fraudulent activity by up to 37% in organizations that adopted the technology. This kind of efficiency allows businesses to secure quick wins while continuing to scale AI applications over time.
Staying in the short term, Steinberg tells the Emerj executive audience that he sees AI delivering substantial ROI through improving anomaly identification workflows as a rule. These use cases include fraud detection and span anti-money laundering and know your customer (AML and KyC) efficiencies as well.
Steinberg underlines what is often forgotten about the nature of these workflows is they offer a near-instant payoff by helping financial institutions identify suspicious transactions and prevent fraudulent activities before they cause significant damage, and their inherent data exhaust is perfect for developing models to identifiy greater efficiencies and insights in essential enterprise data. Steinberg notes that AI’s ability to analyze massive amounts of transactional data in real time makes it far more effective than traditional rule-based systems.
Steinberg explains the reason is that implementing AI for fraud detection not only offers immediate financial benefits but also boosts customer confidence. By protecting their clients from fraud and demonstrating a commitment to security, financial institutions can foster greater trust and loyalty among their customers. These proven gains, in turn, enhance the institution’s reputation and competitive positioning in the market.
Ensuring Long-Term Scalability and Value
While short-term ROI is crucial, Steinberg offers a vision of AI’s long-term potential in corporate accounting workflows and beyond that’s even more transformative. He cautions executive listeners that AI adoption is a constant evolution, not a one-stop process. In the long run, AI enables predictive analytics, advanced financial modeling, and long-term risk assessment, allowing organizations to stay ahead of market trends and shifting economic landscapes.
Steinberg continues that, to be successful, AI must be embedded into the core functions of finance teams to unlock its full potential. That scale of embedding means integrating AI both capabilities and insights into strategic planning, compliance, and investment analysis. Steinberg tells the audience he believes there are is a healthy portion of the global market B2b that have been guilty of not deploying this type of technology to the extent that it can be beneficial to them. By using AI to model future financial scenarios, businesses can better anticipate market shifts, improve risk management, and drive sustainable growth.
Another challenge Steinberg highlights is the fear of job displacement that often accompanies AI adoption. While automation and AI can indeed optimize tasks that previously required manual labor, there is an ongoing debate about how these technologies will affect employment in the finance industry. Steinberg exempts that, rather than eliminating jobs, AI in finance can augment human work by taking over routine, repetitive tasks, allowing professionals to focus on higher-value activities such as strategic decision-making and client engagement.
Steinberg emphasizes that using that argument to demonstrate the agility necessary to scale AI across different functions within an organization is key to unlocking long-term ROI. As AI models learn from more data, they become more accurate and offer deeper insights, particularly in areas like investment analysis and financial forecasting. “It makes sense to allocate time and some resources to be on the forefront of what’s happening,” Matthias tells Emerj, “and make sure, as a finance team, you’re always working on initiatives that test AI toolings for your processes and for your finance department.”
A recent McKinsey study found that companies using AI at scale can increase their cash flow by 120% within five years. Confronted with scaling trends, Steinberg’s advice is clear: businesses that integrate AI into their long-term financial strategy and continually refine it will reap the most significant benefits. Refinement includes aligning AI with governance protocols to ensure compliance as well as regularly training teams to maximize the technology’s value.
Steinberg is again quick to point out that, while the benefits of AI are well-documented, implementing these solutions within financial institutions is not without its time-consuming challenges. One of the biggest hurdles is the complexity of integrating AI systems with existing technology stacks and ensuring that the data feeding into these systems is accurate, clean, and compliant with regulatory standards. Poor data governance, for example, can significantly hinder AI performance and lead to costly errors.
