Chief financial officers and other executives at private equity firms will be able to quickly assess the financial health of a business in real-time using blockchain and artificial intelligence, executed through machine learning and deep learning.
The technology is still in its infancy, but the capacity to review predictive results of companies is being developed, which will help private equity executives to make better long-term decisions on their work and on their portfolio companies.
Executives will be relying on blockchain and AI, machine learning and deep learning financial software programs to gather and analyze extensive data. They will be using machine learning and deep learning to weigh thousands of known factors – from seasonal shifts to industry timings based on historical data. The result will be more accurate assessments of individual companies.
Most accounting and auditing work is still manual and time consuming; blockchain will work to enhance the double-entry bookkeeping by real-time journal entries. Among the companies with finance text analytic solutions, SAP provides blockchain-as-a-service for easy and low-risk experimenting with cloud-based distributed ledger technology.
How does it work?
Blockchain allows companies to write their transactions directly into a joint register (instead of keeping separate records), which creates an interlocking system of verifiable and enduring accounting records. These cryptographically sealed transactions cannot be falsified or destroyed, and in many ways are more secure than a notary. Executives no longer have to wait for quarterly, semi-annual and annual reporting.
Blockchain fundamentally provides a source of trust, secures the integrity of accounting records with completely traceable audit trails, and creates fully automated audits. The accuracy and immediacy of the distributed ledger reduces the need for time consuming multiple audits of companies with multiple ledgers.
In such a system with complete transparency and real-time reporting, there are no incentives to delay hiring, advertising, and implementing projects to the end of the quarter. The timing can be based on what’s best for the company, not the financial reporting schedule.
As far as potential hurdles, new governance will have to be determined around who gets access to the information, since maintaining transparency is part of the strengths of blockchain. There will also be “switching costs” to move financial statements into blockchain distributed ledgers in the cloud. Lastly, financial practices will have to move all transactions that have in the past existed outside the ledger and move them into the blockchain ledger.
Beyond distributed ledgers
After distributed ledgers are fully established on the blockchain and in the cloud, investors and private equity firms can run this data via deep learning, referencing historical data and inputting thousands of other variables into the neural network. This means that over time, the accuracy of predicting the accounting direction of the company becomes more realistic.
Investors will be able to have an end-to-end view, and can look over the entire portfolio, by reviewing the growth and expenditures that have been analyzed by machine learning and deep learning. This end-to-end view will suggest what kind of company should receive investments based on its future growth potentials.
Some behavioral biases can cause investors to make mistakes. Using machine learning and deep learning will potentially safeguard from these human errors, or at least call attention to a reality based on a whole new type of software that continuously learns and improves every time new data is inputted (without being explicitly programmed).
The analysis of investor portfolios and predicting five years in the future, recognizing trends in the market – all become more feasible and accurate with blockchains and AI, machine learning and deep learning. Managers will be able to more easily observe portfolio risks and data drifts, giving them greater insights.
Using blockchain real-time journal entries, private equity managers can statistically see the burn rate of a specific company and analyze its meaning. For example, a company may show a loss of $100,000 a month in its first quarter, indicating overspending. However, this statistical analysis may be wrong.
A truer picture comes through machine learning and deep learning technology, made possible by the increase in granular data as devices such as laptops, cell phones and tablets became household items. As a result, factors such as location, weather, demographic trends, psychographics and common industry patterns can be used for machine learning and deep learning outcomes.
These machines are being trained and can predict more accurate results. So a statistically true result can now be refined, and in fact may show something completely different. For the above example, the company that was operating at a loss in the first quarter will have an enormous increase in profits in the second quarter, since their clients make their major purchases in the second quarter. AI will eventually be able to fill in the information gaps quickly, going far beyond the scope of human capability.
The complexity of predicting investments increases when looking over an entire portfolio and gauging results over five to ten years. Using semantic analysis (identifying what decisions made by machine learning and deep learning) and comparing results over time, AI can predict events that can help private equity in their business decisions. In a global scenario, for example, there are millions of variables that can impact business — these variables come from macroeconomic factors, global political conditions, global trade environment and so on. These data points are readily available on the internet today, but it is impossible for a human to factor in all the massive data and make a pattern out of it. AI can help solve such data problems.
AI can examine portfolios with a more holistic view, can review any number of companies at once and identify patterns that can advise private equity officers on their most critical decisions. For example, a company that is projected to start operating at a 10 percent loss in two years (based on AI analysis) will inform the private equity executive that it may be advisable to sell in one year. AI will be able to also alert an executive to fill in their fund portfolio with a particular type of company that is missing.
Blockchain-distributed ledgers are set to become the next version of accounting systems. Similarly to how the internet became an integral part of every financial business, blockchain will have the same type of impact in terms of how transactions are recorded, reconciling information across businesses, and enabling new infrastructure for financial products and services. As these tools get smarter and faster, private equity companies will gain deeper and real-time insights in their current evaluations of their portfolio, while enabling more accurate predictive decision making for their long-term value estimates.
Vishal Shukla holds more than 25 patents in the field of software-defined networking, IOT, cloud automation, cloud security, cloud orchestration and in cloud performance areas. He is the senior director for deep learning solutions at NVXL Technology in Fremont, California and has held various executive and leadership roles at IBM, BNT, Cisco Systems, Nortel Networks and Infosys Technologies. He is the author of several books on SDN, OpenFlow and OpenStack technologies.
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