How to improve audit with AI?

As scandal after scandal have destroyed audit credibility, the audit profession needs a total restructuring. One type of restructuring happened when PCAOB (regulator, Public Company Accounting Oversight Board) took control away from accounting firms’ self-regulation. The ongoing problems with audit are now forcing the exploration of new ideas. Some believe that the large accounting firms should be fragmented into smaller firms and must require the inclusion of smaller firms as partners. Others are suggesting having government take over the entire audit business (like IRS for taxes).

Audit suffers from both effectiveness and efficiency. In fact, the problem with audit is that effectiveness and efficiency goals tend to work against each other. If you seek efficiency, you may have to compromise on effectiveness and vice versa. Machine Learning can greatly improve audit outcomes. The application of machine learning happens in all stages of audits. Machine learning can also help discover new business models for audit firms.

Audit automation can be viewed as automation of audit planning, audit evaluation, internal controls risk assessment, reporting, fraud detection, valuation, and other such audit process tasks. AIAI offers a report on machine learning in audit.

Machine Learning for Bond Pricing

ML and Bond Pricing

Bond origination and OTC trading continues to be manual, risky, and problematic. The problem comes from figuring out how to price the bond. It is not easy to measure credit risk. There are multiple liquidity buckets and different issuers. Credit risk assessment is a major problem and it impacts price discovery.

Measuring credit pricing risk and valuation requires using many different datasets. Dynamic monitoring of credit risk implies using dynamic data that includes factors such fundamentals, alternative data, transactional data, and behavioral data to price risk.

Many models are being developed and deployed. Some are based upon older machine learning techniques including support vector machines, regression, and k-nearest neighbor. Deep learning and neural networks are now being used to improve the outcomes.

AIAI offers a comprehensive report on these developments.

How Covid19 has Accelerated and Changed Data Needs?

Covid-19 has changed the world. The unthinkable has happened. The world has just experienced the strangest and most unexpected times. This is placed our businesses, industries, institutions, and countries on different trajectories. In some areas, growth has accelerated. In others, it has stagnated. The structural changes in the economy are large enough that asset managers, investors, firms, analysts, and strategists need to reanalyze their assumptions.

Using pre-Covid assumptions for the post Covid-19 world is an extremely risky undertaking.

The data acquired and produced during these times will be critical for many types of analysis and machine learning solutions. Let us look at some examples:

  • Marketing Data: Consumer purchasing data gives tremendous insights into how stress, pandemics, physical localization, and change impacts our buying habits and purchasing behaviors.
  • Retail Data: Many US retailers filed for bankruptcy. Others saw an increase in sales. Chinese and Europe retail sectors are improving but the pre-Covid19 business is different than post-Covid19.
  • Supply Chain Data: The emerging geopolitical situations, rise of nationalism and new rivalries, and now Covid19 – all are pointing that 2020’s will the years of realignment and restructuring of supply chains. Supply chain data will be critical to assess and predict the new reality.
  • Trading Data: Search for alpha signal is one area of exploration. The other is behavioral information. The information about how investors and machines behaved during the Covid19 turmoil is embedded in the data.
  • Geopolitical Data: Geopolitical risk is increasing. With treaties (including nuclear treaties between Russia and US) falling apart, increasing conflicts in the Middle East, and a new West-China rivalry taking shape, the geopolitical risk assumptions need to be revisited.
  • Healthcare Data: Covid19 has given the healthcare industry significant data to analyze and develop new therapeutic and diagnostic solutions.

The bottomline is that it is time for data suppliers to create awareness that Covid-19 has changed the underlying assumptions related to markets, behaviors, competitive structures, and industry and company performance. New analysis backed by new data will help understand the shift.

Why strategies developed in labs fail to perform in real life situations?

Hundreds of papers are published in finance that claim to discover successful new investment strategies. However, when the strategies are deployed in real market situations, they often fail to perform as they did in the lab. Some of the major reasons for their failure are as follows:

  1. The real-life market data tends to be very different than the data used in the labs. Lab data contains full and corrected data whereas in actual situations data is delayed for several minutes or hours after markets close.
  2. Data is sometimes corrected in periods (days, weeks, or months) after the periods. For instance, data might contain corrected (restatements) earnings.
  3. The sampling of data may not be random as the model developer has ex-post knowledge of events and hence it is likely that data taken is from normal times and not when markets experienced abnormal periods.
  4. Other than the tick data, other data – especially fundamentals data – is extremely limited.
  5. While a pattern may be discovered but without proper economic foundations, it becomes too risky to deploy in production.
  6. Model works well with the data used by the modelers but fails to generalize (overfitting).

For those reasons, it is important to have standards developed for machine learning in finance. AIAI is working to establish those standards.

AIAI-Newsletter July 13 2020

7 Opportunities in 7 Minutes on Day 7

Your Sunday Briefing starts here:

Opportunity 1: Don’t forget the investment banks

What is on the radar screen?

Do you know there are several powerful opportunities to transform investment banks with AI? Investment banks are looking for creative solutions to their problems and AI/ML can directly help. After a seven-year M&A boom, Covid19 has greatly reduced the M&A activity. With large revenues gone, smaller and medium sized investment banks are feeling the pain. Large shops have diversified income streams from trading and capital markets, so they are better positioned to handle the storm.

Smaller boutique firms hold significant market share of M&A deals in the industry. Covid19 has shrunk the deal volume. Boutique firms also offer many other types of services – such as capital raise, debt, and risk management.

Why it matters?

Post Covid19, the old model of investment banking needs to be jumpstarted. Many mid-sized and smaller firms have the intellectual bandwidth and relationships to close the deals, but they need to transition to the digital age. This means to have intelligent agents that can perform initial investment banking work, attract potential client interest, outline opportunities, and be able to demonstrate value in a virtual environment.

What’s the scoop?

The scoop is that many leaders are confused and seeking clear guidance. There are budgets to transform the firms.

How to do it?

Help transition mid-sized banks and smaller investment banks into world-class digital deal shops. Strategic transformation requires building the following capabilities:

  • Identify opportunities based upon publicly available data
  • Proposal building
  • Due diligence of the business
  • Automated valuation
  • Evaluate and analyze the deal
  • M&A document preparation
  • Develop insights about potential acquirers
  • Perform detailed due diligence
  • Valuation readjustment
  • Negotiation strategy development
  • Document review and management
  • Transaction management

Opportunity 2: There’s lots of green for AI in the Green bonds

Is your firm well positioned to target a $1 Trillion industry? If your sales teams are not familiar with ESG, get them ready fast. ESG is one of the greatest opportunity for AI. Here is the scoop.

What is on the radar screen?

ESG stands for Environment, Social, and (Corporate) Governance. ESG has become a major phenomenon in the finance sector. Companies issue ESG performance reports. Asset Management firms look for companies that are responsible in terms of ESG. Investors seek portfolios composed of ESG firms. Mutual funds, ETF, and other investment vehicles are designed for responsible investment. And there is an entire category of green (sustainable) bonds.

Green bonds market has seen significant expansion while simultaneously being criticized for lacking oversight, audit accountability, and greenwashing. Greenwashing implies that issuers are not really serious about benefiting the environment.

Why it matters?

There is over $1 Trillion of sustainable (green) debt. While everyone is rushing into ESG, the foundational elements of ESG are not well developed. For example, clear standards may not exist for what classifies as good performance in ESG. Identifying good ESG opportunities, developing standards, managing risk for ESG, and valuing ESG securities are powerful problems that only AI can solve.

What’s the scoop?

The inside scoop is that anxiety is growing and a lot is at stake. Wrong valuations mean bad investments.

How to do it?

Target investors, underwriters, and issuers and inform and educate them on how they can leverage ML/RPA to solve the following problems:

  • Identify green opportunities
  • Use ML/RPA for materiality assessments
  • Deploy valuation frameworks for assessing credibility scores
  • Help clients pick the right securities with ML
  • Identify what the right standards should be
  • Understand and manage the risk for green bonds

Opportunity 3: Sharpening the knives, preparing turnaround firms

Hidden within the highly lucrative layers of the finance industry is a powerful subsector of turnaround and restructuring. Partly legal, partly business and strategy – restructuring and turnaround firms (and departments) are getting ready for what lies ahead. They need ML to create their competitive advantage.

What is on the radar screen?

High risk corporate debt was a major problem even before the Covid19. Covid19 has made the problem worse. Companies had to seek incremental financing and many investment banks (Goldman and Morgan) saw an increase in Q2 2020 revenues from that activity. However, part of the Fed intervention to save the economy comes in the form of buying high risk (junk) bonds. This is done to save the corporate bond market from collapsing. The process began in May of 2020 and it has shrunk distressed debt from $750 billion to about $400 billion. But this cannot go on forever. This means significant restructuring is coming and companies need AI tools to prepare for what lies ahead.

Why it matters?

U.S. high-yield bond market size is nearly $1.2 Trillion. Investing in this market is one area where ML will be the only effective solution. However, the real area of high opportunity is the upcoming distressed debt investment and bankruptcies.

What’s the scoop?

The inside scoop is that restructuring and turnaround firms competed based upon their size and knowledge base. But now things could be different. The ability to analyze debt, valuation, exploring strategic options, bankruptcy case preparation, and management during bankruptcy are all automatable areas. Firms that invest in these areas will become the new industry leaders.

How to do it?

Target issuers, investors, and restructuring and turnaround firms and inform them about the power of ML/RPA to transform the following processes:

  • Identify clients (opportunities for restructuring)
  • Create options for clients
  • Help with Valuation of distressed debt
  • Analyze bond for comparative structures
  • Analyze bonds covenants
  • Perform Bankruptcy analysis and options discovery
  • Perform Bankruptcy case preparation
  • Discover Distressed debt buying opportunities
  • Create pathways and maps for renewal

Opportunity 4: Cat Bonds and Insurers

Do you know that ML/RPA can play a huge role in driving the future of the insurance industry? No, this area is not about cats that meow and purr and have nine lives. It is about premiums and payouts (often) related to one life. Here is the scoop.

What is on the radar screen?

Even before the Covid19 catastrophe, the insurance business was having bad years due to floods, fires, and storms. Then comes Covid. Insurance Linked Securities (ILS) suffered. Cat Bonds (catastrophe bonds) are high-yield debt instruments that are used to raise money for companies in the insurance industry in the events of natural disasters.

Why it matters?

With $36 billion of cat bonds outstanding, and crisis after crisis, what’s there not to worry about! The market has come to a halt and only innovation can jumpstart the market. 

What’s the scoop?

Imagine the problems with these securities. How to value them? How to price the risk? Reinsurers who package and sell these products are finding new ways to attract the market. But investors are cautious. ML can help all the players in this subsector.

How to do it?

Target insurers, reinsurance shops, and investors and show them how ML/RPA can revolutionize the industry:

  • Mortality and catastrophe forecasting
  • ILS pricing
  • Payout management
  • Terms and conditions – bond covenants analysis
  • New structures (for structured products) analysis
  • Risk assessment and valuation

Opportunity 5: China, China, and China

This is an area that requires immediate ML/RPA help. No, it is not about opening an office in China – it is about helping your non-China based financial services clients evaluate and estimate the risk related to the Hong Kong problems.

What is on the radar screen?

Against the backdrop of protests and growing US-China rivalry, China passed the Hong Kong security law. Many countries condemned the move. Caught in the middle are the firms who have significant exposure to China. First, do your clients know which firms have higher exposure to China? Second, how do you measure the exposure?

Why it matters?

With a $14 Trillion nominal GDP, China is the second largest economy in the world. While things were fine a few years ago, more recently China and US seem to be in a lock horn position. Geopolitical issues do have consequences. The question for your clients is how will they get impacted?

What’s the scoop?

Financial services providers are concerned about how to analyze risks related to China. With tradewars and policy quarrels, the risk related to doing business with China has changed. ML can help. 

How to do it?

Target your financial services clients and help them make sense of the Chinese situation with machine learning with the following solutions:

  • How to develop insights into the Chinese situation
  • Risk estimation
  • Business risk assessment
  • Investment risk assessment
  • Alternatives risk
  • Pricing geopolitical risk in securities
  • Discover who is exposed
  • Discover how they are exposed

Opportunity 6: Covid-19 Earnings and Portfolios

Covid-19 earnings reporting season is beginning. The traditional measures of risks have changed. Companies, sectors, and industries are experiencing structural changes. Only ML can help develop the forecasts necessary for portfolio realignments. Is you sales team ready to have a real conversation about earnings and portfolio realignment? The discussion below can help.

What is on the radar screen?

As companies announce earnings, the announcements will contain signals about whose business has been impacted in what ways. Analysts will try to determine which impacts are permanent and which are temporary. They will need to assess how the fundamental structures of various industries and economies have changed. Money will follow the growth opportunities and risk will be repriced.

Why it matters?

Covid-19 has ushered the global economy to a new reality. It has produced new winners and losers. However, not every loser is gone forever and not every winner will stay as winner. Too much change is in the air and ML can show the way out. 

What’s the scoop?

Asset Managers want to understand the dynamics of the new reality. Many lack appropriate tools.

How to do it?

Target asset management firms to explain how ML can help in the transformation. Build the following capabilities for clients:

  • Fundamentals forecasting based upon Covid-19 factors
  • Behavioral modeling
  • Structural changes in the economy
  • NLP based tracking of risks
  • Supply chain impacts
  • Business model transformations

Opportunity 7: Social Injustice and Diversity matters for your clients

The major protests in the United States against racial injustices have influenced financial services firms to rethink their internal policies, procedures, and HR frameworks. ML/RPA can help create fast value. If your sales teams are too timid to knock on the doors of the HR departments of financial services firms, they are at a disadvantage.

What is on the radar screen?

The nationwide protests and racial justice and equality movement has left many financial services firms to reconsider their ways of doing business. But this reflexivity goes beyond just HR as many are concerned about their policies related to issuance of debt, product designs, governance, and compliance.

Why it matters?

The racial justice movement is not going away. The movement has spread beyond the United States and across the world. United Nations has recently issued a statement. For many people the root of injustice often lies in access to financial services. There are hundreds of issues that would need to be resolved. ML can accelerate the journey. 

What’s the scoop?

For the past one month, this has been the most talked about topic by HR specialists (even more than Covid). Companies who will build a beachhead in this category will not only have great business opportunity, they will also pursue responsible business practices.

How to do it?

Start with HR and then access the operations teams. Target HR departments of large financial services firms and show them how ML can help increase diversity, enhance social justice, improve race relations, and create a healthier environment. Build the following capabilities for clients:

  • Identify issues from policies
  • Identify issues from language
  • Identify issues from organizational analysis
  • Discover the image of the firm in terms of racial justice
  • Analyze complaints
  • Analyze lawsuits
  • Analyze management and board discussions (from 10k etc)
  • Measure diversity

Commerzbank: Opportunity for AI centric transformation

Under pressure from private equity group Cerberus, Germany’s second largest lender began a restructuring process last year that targeted a meager 4% RoE by 2023 – significantly less than the investor expectations (cost of capital) of 10% return. The Covid19 crises led to a €295 million loss in the first quarter of 2020. As American celebrated the Independence Day holiday, the CEO and the chairman of Commerzbank have left. Their leaving is widely seen as a move by Cerberus, the second largest shareholder has been able to influence the exit of both the CEO and the chairman.
Based upon reinventing the firm, there is significant momentum and appetitive for total transformation. With that type of board and management support, this is the best time for Commerzbank to rethink its future and build it upon an AI centric transformation.
As the bank recognizes the importance of AI as a new paradigm for transformation, the mindset needs to evolve from viewing AI as a technology to support the strategy to seeing AI as the central strategy. This is a great opportunity to transform the bank with full scale automation. The restructuring plan was developed by a consulting firm; however, an AI centric transformation plan is different

Unlike typical deterministic planning that goes in typical strategy models, AI centric transformation is performed with a flexible and creative mindset. Creative planning happens when firms view AI as a simultaneous transformation of business model, business strategy, operational plans, and process automation. In that type of planning, firms develop competitive advantage through a systematic exploration of data.

In the past, several IT initiatives were launched by Commerzbank. Even though some of those initiatives were successful, they were not able to change the trajectory of losses or to create a meaningful competitive advantage for the firm. The use of AI remained at a tactical level. The current change offers an incredible opportunity to rethink and plan the future of Commerzbank.

Going Social and Quantamental with AI

Marshall Wace uses MW TOPS trading system which collects investment ideas from over two hundred sell side institutions and independent research providers. With millions of trades conducted on the platform, the TOPS architecture allows for global, diversified portfolios with differing risk and trading profiles. With the TOPS architecture, the firm is able to offer both fundamental and systematic styles of investment and integrated them to alpha.

MW claims to focus on ‘Quantamental’ investment tools which find trading signals in complex data patterns. Marshall Wace says that the firm uses innovative dataset knowledge and systematic investment discipline to augment and improve its fundamental portfolio management approach. The firms also claims the development of entirely new strategies from data.

MW has now announced that it is raising a $1 Billion fund. It is expected to be part of the TOPS system. The real questions are:

  • What would it mean to integrate ESG with quantamental strategies? In other words, in addition to fundamentals and systematic or market based strategy development, integrating ESG would add a third factor.
  • The second question is what would an AI solution look like when ESG is integrated with quantamental?

Discovering and identifying investment strategies is a complicated business. Add to that the ESG component and the job becomes extremely hard.

AIPOST has a custom report that answers the above two questions.

On Value Investing and AI

With the rise of growth investing, and a Fed that comes to the rescue any time there are any signs of trouble, is value investing dead or dying? Value investing is based upon the ability to discover undervalued companies through finding the difference between intrinsic value and market value and waiting for markets to correct. The traditional approach (Benjamin Graham) of value investing focused on margin of safety and quality of investment. Warren Buffett focused on competitive advantage (moats) to identify quality assets. In addition to discovering the attractive assets, value investing now includes:

  • ESG: How to include social responsibility as a variable in determining quality?
  • Across multiple asset classes: How to identify value across different asset classes?
  • Reviewing structural dynamics: How do the economic structures, supply chains, technology adoption, geopolitical environment, and other macro-economic variables have on value?
  • Behavioral: How do behavioral variables factor into value investing?
  • Signals: How to capture and use new and exciting alpha signals?

This leads us to what can be described as the deep value investing. Applying deep learning to discover the fundamentals of value creation and then aligning them with the extended signals is where the future is. With AIPOST you can discover how to?