Many of us have been swept away with the rapidly increasing adoption and interference of technology in our lives. Well, corporate finance is no different. The significant developments in AI technology is expected to augment existing business models of advisory firms and, in some cases, form new business models through the digital revolution.
One of the underlying causes of this widespread technology adoption is the massive increase in data from across society, including texts, sensory information, numbers, images, etc. While, the other reason is the increasing availability and affordability of cloud-based processing for analysing all different types of data at speed. As corporate advisory firms become more sophisticated and diverse, the potential for AI usage in transactional activities increase subsequently. In fact, AI-based technologies are expected to have a marked impact of $4 trn on the global M&A market in the next decade for more than 50,855 corporate transactions.
Though AI represents an undeniable threat to some of the rather traditional activities in advisory services, it will also lay the groundwork for new employment opportunities, where much broader intelligence, expertise, and complex interaction is required. Considering that clients are demanding ever-more speed and extreme eye-for-detail, AI technology could be the answer to a faster, more accurate and insightful advisory service.
An important question to ask is how AI-based technologies will add value to companies, investors, advisers, and to the greater society, including the overall economy. In the short to medium term, it is highly unlikely to observe radical changes and new business models for corporate finance. However, in the long run, AI will flourish for innovative collaborations and combinations of advisers, consultants, technologists, as well as clients.
AI in a global context:
Artificial Intelligence, short for AI, is a technology that can more or less imitate some aspects of human learning, reasoning, problem solving, planning, and self-correcting. AI as a ‘general purpose technology’ can trace predictive analytics, patterns, and aspects of complex decision-making. These highly sophisticated features of AI will change not only what organisations already do, but also how they achieve it, resulting in a transformative effect in the way corporate transactions are handled.
The range and depth required for corporate transactions and investment has risen tremendously in the past decades. The most forefront for specific data is accounting firms: compiling, organising, analysing, and reporting on huge quantities of business and financial data. The increasing demand for filtered and detailed data pushed accounting firms to create technological tools for analysis and to innovate new methodologies. Another business that flourished in AI is Goldman Sachs, as the firm is reported to be automating its own IPO processes through recognition of 127 computerisable steps.
According to the Governor of the Bank of England, the banking industry is expected to invest a further $10 bn on AI-systems in 2020, making it the second biggest global investor on AI systems after retail. It is worth noting that, this investment will have significant benefits in terms of fraud detection, automated threat intelligence and prevention, credit assessments, and wholesale loan underwriting and trading.
How much data is too much data in Finance?
Corporate finance activity is heavily centralised on data, which creates discrepancies in quality and standardisation. Data should be mined, sculptured, and filtered in order to be useful in AI applications. When data is cleaned up at source and stored for transfer, it is standardised for accurate usage. One aspect of deal transactions that will benefit the most from this standardisation of data is taxes. If tax were to be simplified, though complex data will still require expertise, there would be fewer grey areas and more deliberation amongst professionals.
But how can data be filtered and standardised? Aiming to increase efficiency and reduce headcount in laborious manual tasks, many firms started utilising the robotic process automation (RPA). RPA is commonly practiced by finance teams to streamline reporting and transactional jobs, which would leverage more time for skilled professionals to perform tasks of expertise. Another example would be automating the process of rectifying invalid or incorrect invoices, strengthening efficiency and accuracy. For technical services, machine learning can be utilised to evaluate the quality of regulatory filings and to identify anomalies.
The use of RPA in finance is still at the very start. The potential for RPA to evolve and improve will occur substantially with new developments in AI arising.
AI in Corporate Transactions:
For corporate transactions, major investment banks are experimenting with AI to profile customers, identify demand trends and forecasting sales, and find more “buy” or “sell” signals, including in-company reports. For instance, IBM’s Growth & Transformation Team has been developing tools based on IBM Watson to assist M&A researchers with match-making services for target companies. At the same time, venture capitalists are rumoured to be implementing AI to identify attractive start-ups. The New York-based Axial Networks is a new generation example to online service providers making use of AI algorithms to recommend the most relevant parties for buyers/sellers to approach. The firm takes into account each buyer’s and investor’s real-time intent, in addition to, their strategic and financial interest on both ends of a deal.
The industry is still in the midst of experimenting with Artificial Intelligence in its transaction processes. Many spectrums of corporate finance are affected by the developments in AI:
- Investment: The main concern of clients are savings, thus they seek the expertise of financial advisors, whom are not always right on their advice. In the wrong communication, advisers could even tarnish their relationship with a prospective client. This is where robo-advisors kick in and propose a resolution. The robo-advisors work in a sophisticated matter of collecting information about investors. This information can be their financial goals, the level of risk they are ready to take, etc. After data has been assembled into algorithms, robo-advisors can provide personalised investment paths to the user, which also has higher chances of acceptance. Through this technique, asset management costs can be minimised, alongside lower human errors and conflicts of interest.
- Customer Engagement: The transparent and frequent involvement with the clients is ensured through chatbots that are AI enabled bots capable of responding to customer queries related to their financial transactions.
- Fraud Detection and Risk Management: Banking sector is one of the highest ranked industries to have been poorly affected by cyber fraud. Banking frauds are reported on a daily basis across the world, while this AI can detect this fraudulent behaviour way before any human analyst. To illustrate, if one were to log in to his/her internet banking platform from a different location than usual, AI detects this and asks for further identity confirmation. When identity mismatch detected, the system is temporarily blocked and customer is protected from a potential fraud. Though a controversy arises from AI being used as a solution when fraud mainly occurs from online banking systems created for the convenience of the customer.
- Personalised Financial Products: The benefits of AI take the banking industry to a more personalised era. Once sufficient data on spending and saving is collected, banks can advise customers on various financial products, such as a loan plan, mortgages, auto loans, and other financial products. In the condition that the right service has been offered, AI can promote debt/equity swap.
- Strategy and Corporate Finance: When setting up the optimal strategy for corporate finance, AI technology can analyse the credit history of a company and make suggestions on the best loan offer possible.
- Business Acceleration: Banks can utilise AI to expedite knowledge-based processes to enhance efficiency and performance. AI has tremendously reduced the cost of financial services and made banking far simpler for users, ultimately, creating value: business acceleration.
AI in the Deal Process:
What is the extent of corporate finance advisors integrating AI-based applications into potential acquisitions, buyout targets, or prospective investors? The increasing interest in creating accessible tools for institutional and private investors is accelerating developments in the digital services on the market for personal savings and investment. These services include helping build portfolios of investments in public and private companies, but also in real estate and other asset classes.
Many aspects of the deal transaction have been affected by the developments in AI:
- Negotiations: Is it possible to have AI negotiate for humans? When clients are demanding ever more speed and efficiency, the negotiation part of a deal could be speeded up with the right classification of data to identify what is holding back an agreement. If the right data could be collected to find the clauses in contracts that are causing the bottlenecks, the process could be worked out quicker. The algorithm created to analyse previous contracts could reveal superfluous clauses that are critical for the overall negotiation.
- Precedent Transactions: Though the building blocks of the underlying technology are well-established, there needs to be sufficient training data to build an accurate system. As developments progress, AI will be used effectively to analyse past deals, connect success and failure factors, and identify preferred characteristics of future deals. Firstly, AI will carry out “pre-emptive diligence” by analysing publicly available data on thousands of companies prior to a private equity investment or corporate acquisition. An alternative and more sophisticated approach would be to integrate actual deal outcomes to the characteristics of a target company, using detailed and large data- though the availability of data is scarce.
- AI & Due Diligence: The potential for Machine learning application in legal due diligence is already the subject of many R&D projects. The legal due diligence requires to go through thousands of documents in order to identify risks, actual liabilities, and potential liabilities for acquirers of or investors in a company. AI-based tools can automate many parts of that process, including the location of documents, their identification, standardisation, indexation, and ranking for relevance. For financial due diligence, the industry has been an early adopter of Microsoft Excel and Tableau software for digital visualisation.
- Company Valuation: Valuation is a natural area for which AI is used to recognise comparable businesses and assets, develop suitable metrics and automated analytical tools, producing outputs proofread by experts. One company that took the lead, KPMG is experimenting with AI-tools to probe the extent to which judgement can be reduced at valuations. However, valuing a business is complex, while high-quality data on actual comparable companies is not ubiquitous.
- Deal completion: Machines have the ability to be less fallible than humans in this area of high technicality when sealing a deal. Since deal completion is subject to negotiations itself, precedent transactions can be aggregated and analysed in terms of insights and learnings to automate the deal completion.
- Post transaction to exit/divestment: After an M&A deal is closed, PE firms can take advantage of AI for further opportunities: deriving more value out of companies, identifying operational improvements, recognising unbilled revenues, or observing EBITDA in case of price increase. The analytical tools of AI will foster new ways of creating value and will prosper deeper in operational processes.
Changing role of virtual data rooms:
One of the most important applications of AI-based technologies is virtual data rooms (VDRs). VDRs are at the centre of multiple major corporate transactions and even private equity-backed deals. The purpose of VDRs is managing and controlling online access to data related to the due diligence. These data rooms ensure risk management; however, they are not risk-free themselves, thus require expertise.
Drooms is a VDR provider that grants secure access to confidential documents. To ensure a high standard and efficiency, VDR platforms are using AI and Machine Learning to assist with deals of all size. Drooms has even developed Application Programme Interfaces (APIs) that connect with IT systems of investment banks and advisory firms. Further more, Drooms has deployed AI-based technology to analyse and filter content, so that experts can go through and evaluate extensive data rapidly. Additionally, the company has created a “deal lifecycle tool” specific to private equity firms to assemble a controlled, standardised environment for documents regarding deals and portfolio companies. This tool can assist with portfolio tracking and reporting, which can accelerate exit opportunities.
The search function of VDRs take advantage of Machine Learning. Based on templates created by Drooms or customised by a client, the search function leads to relevant findings. The system also allows the reviewer to make additions of information or analysis, which trains the system in the background. Through this review contribution, the system gets smarter and the search outputs yield more relevant results.
According to research, the future of corporate advisory and transaction services lies in the hands of AI-based technology with an average score of 9/10 for its potential significance. However, though very crucial for finance, professional services advising on corporate deals are far from developing their own AI capabilities with a rate average of 4/10 for current application. Though firms have a long way to go in current implementation, corporate and investment clients need even longer with a score of 3/10 for current adaptation.
In a global AI market dominated by China and the US, other countries should push for further implementation and adaptation to AI-based tools and techniques in corporate transactions. In order to keep up with the AI-market leaders, others should consider increasing public and private investment significantly, as well as, promoting new forums of R&D collaboration for the new era that is Artificial Intelligence.
For the greater goal of reaching the desired level of AI applications in corporate finance, not only firms and individuals, but also governments around the globe should take immediate action to keep a competitive-stake and invest in AI to further-boost private investment.