UniCredit Leads the Rise of AI-driven Mergers & Acquisitions

European financial services giant UniCredit recently launched its new digital system DealSync, an AI-powered platform designed to identify and manage smaller mergers and acquisitions (M&A) deals. The initiative is part of the company’s ambitious strategy to increase their revenue by €1.4 billion by 2027, without expanding its workforce. So far, the new system has been used to identify around 500 deals, enabling employees of UniCredit’s corporate banking and wealth management department to present potential opportunities to M&A bankers.

DealSync was designed to focus specifically on small- and medium-sized enterprise (SME) transactions, particularly those valued below €50 million. That segment sees around €50 to €60 billion in M&A deals annually in Italy and Germany alone. It’s an area ripe for growth – not just in Europe, but potentially around the world, including the US where it has a branch in New York.

Traditionally, investment banking models have leaned heavily towards major global enterprises, while the other side of the M&A market has relied more on tapping into localized market expertise. DealSync aims to close the gap by focusing on the mid-market by efficiently matching buyers and sellers where potential opportunities often go unnoticed.

Redefining the future of investment banking with AI

UniCredit’s foray into an increasingly AI-centric fintech market exemplifies a significant trend across the financial sector. The integration of advanced technologies, such as machine learning and AI, isn’t just about streamlining existing processes, but also about identifying and capitalizing on new business opportunities. These include those that weren’t particularly financially viable before, as has long been the case with mid-market M&A deals.

AI platforms like DealSync can process immense amounts of data to identify potential deals, making it possible to expand without adding to the headcount. This makes expansion – regardless of the market or use case – far more viable and less risky. In UniCredit’s case, by targeting smaller deals that were previously overlooked, their investment banking clients can tap into new market segments or revenue streams.

For other fintechs, this highlights some important strategic considerations, such as the need to invest in talent and infrastructure to support AI initiatives. Other priorities include robust data management, since access to high-quality data is a must for AI systems. Ultimately, UniCredit’s DealSync provides a compelling case study showcasing how AI can transform traditional banking activities, offering a model that fintechs can adapt to their own needs and enhance their operations and services.