Globally, banking M&A market is showing signs of improvement with 41 percent of companies intending to pursue acquisitions. The value of M&A deals in the Middle East region jumped to $33.7 billion last year, the highest level since 2007 as per Bloomberg
research. While in the European market, M&As have stagnated with changing regulatory structures and banking sector fragmentation at the EU level.
The deregulation of the industry has contributed significantly to the growing M&A activity. With digital transformation sweeping the industry, banks are looking at M&As to increase their digital capabilities and remain ahead of their competition. As per
a report by EY, 53 percent of banks are developing corporate VC arms drive better access to new capabilities and technologies. To supplement traditional banking operations, partnering with FinTech players will be essential in launching new products and services
for the tech-savvy consumers. For instance, French asset servicer CACEIS is in the process of acquiring Dutch Custodian KAS Bank, enabling it to expand its services to local pension funds and insurance companies.
At this juncture, data analytics plays a crucial role in decision making. Banks are looking to big data and machine learning (ML) or artificial intelligence (AI) technologies for help in creating M&A prediction models. While emerging technologies coupled
with advanced data collection and analytics tools are transforming M&A decision-making and derive profitable insights. Business leaders are aided by data insights at every stage of the transaction cycle, enabling deeper visibility, enhanced negotiation terms,
overall improvement of post-deal integrations.
As they strive to increase the scale of economies and capabilities, banks face several challenges during the M&A process. We explore the prominent woes of M&A and how data analytics can help overcome them.
Choosing your target
Almost always, the first step of M&A is to determine a potential target. A sound portfolio strategy would identify the value of potential targets in terms of future revenue streams, disruptive technologies capabilities, customer purchasing power, and a definitive
Data anlaytics can allow business leaders to visualize better, allowing for detailed comparisons and due diligence of potential targets. For instance, a large US bank acquired a competitor with a substantial geographic overlap, expecting increased cost savings
on operations by closing 75 percent of acquired company’s branches. In turn, the deal was unprofitable for the acquirer as they lost customers. Data analysis would’ve helped reveal that most of their customers were heavy branch users and the merger hampered
their access to in-person banking experience.
In this digital era, data forms a core part of operations and strategy for any company. During a merger, the amount of data to be analyzed is essentially double – requiring better tools for managing and analyzing. Integration of legacy systems and handling
data conversions create unexpected costs, disruptions and risks to banks. Additionally, breaking down data silos and connecting disparate data sets would require the adoption of big data and automation tools.
To solve these issues, banks in the M&A process are implementing data management tools to clean and organize existing data and allowing data fluidity for data from acquired firms. An external partner can help integrate systems and reduce risk if data migration.
For example, BNY Mellon rapidly integrated the data of two merging banking organizations, reducing the cost of data integration on asset servicing and combined client base with minimal customer interruption.
Speed and richness
M&As are a time-driven aspect, wherein value propositions supporting business transactions should be made clear to all stakeholders within a reasonable time period. Delay in assessing richly defined data sets could significantly impact efficiency opportunities,
security, and regulatory compliance. On-time insights are essential for effective decision making and accelerate integration execution strategies. For instance, for investment banks, timely data insights can help improve bottom-up estimation of synergies helping
them develop meaningful benchmarks before a deal.
When evaluating potential targets, companies need to take into consideration a company’s culture. A study of 200 senior level banking executives on FinTech and banking M&A found that 41 percent of the respondents cited ‘culture clash’ as a deterrent against
M&A. Traditionally, pre-merger planning and analysis usually focus on the legal and financial aspects of the potential targets. But a quantitative audit of a company’s culture would be essential for successful mergers. Accurate data on cultural compatibility
would help business leaders assess management practices and understand the conflict points; helping them decide the sustainability of the M&A. With analytics, cultural due diligence can be central to the merger process alongside finance and legal parameters.
The high-level assessment would allow business leaders to build on cultural assets, assess KPIs of cultural integration and mitigate risks of a cultural clash.
Today, analytical tools for M&A are built on sophisticated platforms with advanced analytical techniques. As the banking sector goes through a technology inflection point, banks are leveraging the tools to gain value from varied data sources and adapt to
a data-driven digital atmosphere. With the power of data analytics, banks can enhance their due diligence, increase returns, and improve collaboration synergies between the merging companies.