Introduction

Investment research and analysis is beginning to look very different from what it did five years ago. While five years ago, the data deluge could have confounded asset management leaders, they now have a choice on how things could be done differently, thanks to AI and advanced analytics. Advanced analytics helps create value by eliminating biased decisions, enabling automatic processing of big data, and using alternative data sources to generate alpha. 

With multiple sources of data and emerging AI applications heralding a paradigm shift in the industry, portfolio managers and analysts who earlier used to manually sift through large volumes of unstructured data for investment research can now leverage the power of AI tools such as natural language processing and abstraction to simplify their task. Gathering insights from press releases, filing reports, financial statements, pitches and presentations, CSR disclosures, etc., is a herculean effort and consumes a significant amount of time. However, with AI-powered data extraction tools such as Magic DeepSight™, quick processing of large-scale data is possible and practical.

A tool like Magic DeepSight™  extracts relevant insights from existing data in a fraction of the time and capital compared to manual processing. However, the real value it delivers is by supplementing human intelligence with powerful insights, allowing analysts to direct their efforts towards high-value engagements.

Processing Unstructured Data Is Tough

There are multiple sources of information that front office analysts process daily, which are critical to developing an informed investment recommendation. Drawing insights from these sources of structured and unstructured data are challenging and complex. These include 10-K reports, the reasonably new ESG reports, investor reports, and various other company documents such as internal presentations and several PDFs. SEC EDGAR database makes it easy to access some of this data, but extracting this data from SEC EDGAR and identifying and then compiling relevant insights is still a tedious task. Unearthing insights from other unstructured documents also takes stupendous manual efforts due to the lack of any automation. 

10-K Analysis using AI

More detailed than a company’s annual report, the 10-K is a veritable powerhouse of information. Therefore, accurate analysis of a 10-K report would lead to a sounder understanding of the company. There are five clear-cut sections of a 10-K report – business, risk factors, selected financial data, management discussion and analysis (MD&A), financial statements, and supplementary data, all of which are packed with value for analysts investors alike. Due to the breadth and scope of this information, handling it is inevitably time-consuming. However, two sections that usually require more attention than the others to analyze due to the complexity and existence of possible hidden anomalies are the “Risk Factors” and the “MD&A”. The “Risk Factors” section outlines all current and potential risks posed to the company, usually in the order of importance. In contrast, the   “Management’s Discussion and Analysis Of Financial Condition And Results Of Operations” (MD&A) section is the company management’s perspective of the previous fiscal and future business plans’ performance.

As front-office analysts sift through multiple 10-K reports and other documents in a day, inconsistencies in analysis can inadvertently creep in. 

They can miss important information, especially in the MD&A and Risk Factors sections, as they have to analyze many areas to study and more reports in the queue. Even after extracting key insights, it takes time to compare the metrics in the disclosures to a company’s previous filings and against industry benchmarks. 

Second, there is the risk of human bias and error, where relevant information may be overlooked.  Invariably, even the best fund managers would succumb to the emotional and cognitive biases inherent in all of us, whether confirmation bias, bandwagon effect, loss aversion, or various other biases that behavioral psychologists have formally defined. Failure to consider these issues will lead to suboptimal decisions on asset-allocation and often does. 

Using AI to analyze the textual information in the disclosures made within 10-Ks can considerably cut through this lengthy process. Data extraction tools can parse through these chunks of texts to retrieve relevant insights. And a tool or platform custom-built for your enterprise and trained in the scope of your domain can deliver this information to your business applications directly. More documents can be processed in a shorter time frame, and armed with new insights, analysts can use their time to take a more in-depth learning’s untapped potential look into the company in question. Implementing an automated AI-Based system omits the human errors,  allowing investment strategies to be chosen that are significantly more objective, in both their formulation and execution. 

Analysing ESG Reports

Most public and some private companies today are rated on their environmental, social and governance (ESG) performance. Companies usually communicate their key ESG initiatives yearly on their websites as a PDF document. Stakeholders are studying ESG reports to assess a company’s ESG conduct. Investment decisions and brand perception can hinge on these ratings, and hence care has to be taken to process information carefully. In general, higher ESG ratings are positively correlated with valuation and profitability while negatively correlated with volatility. An increased preference for socially responsible investments is most prevalent in Gen Z and Millennial demographics. As they are set to make-up 72% of the global workforce by 2029, they are also exhibiting greater concern about organizations’ and employers’ stance on environmental and social issues. This is bringing under scrutiny a company’s value creation with respect to ethical obligations that impact the society it operates in.

Although, ESG reports are significant when it comes to a company’s evaluation by asset managers, investors, and analysts, as these reports and ratings are made available by third-party providers there is little to no uniformity in ESG reports unlike SEC filings. Providers tend to have their own methodology to determine the ratings. The format of an ESG report varies from provider to provider, making the process of interpreting and analyzing these reports complicated. For example, Bloomberg, a leading ESG data provider, covers 120 ESG indicators– from carbon emissions and climate change effects to executive compensation and rights of shareholders. Analysts spend research hours reading reports and managing complex analysis rubrics to evaluate these metrics, before making informed investment decisions.

However AI can make the entire process of extracting relevant insights easy. AI-powered data cleansing and Natural Language Processing (NLP) tools can extract concise information, such as key ESG initiatives from PDF documents and greatly reduce the text to learn from. NLP can also help consolidate reports into well defined bits of information which can then be plugged into analytical models including market risk assessments, as well as other information fields. 

How Technology Aids The Process

A data extraction tool like Magic DeepSight™ can quickly process large-scale data, and also parse through unstructured content and alternate data sources like web search trends, social media data, and website traffic. Magic DeepSight™ deploys cognitive technologies like NLP, NLG, and machine learning for this. Another advantage is its ability to plug the extracted information into relevant business applications, without  human intervention. 

About NLP and NLG

Natural Language Processing (NLP) understands and contextualises unstructured text into structured data. And Natural Language Generation (NLG) analyses this structured data and transforms it into legible and accessible text. Both processes are powered by machine learning and allow computers to generate text reports in natural human language. The result is comprehensive, machine-generated with insights that were previously invisible. But how reliable are they?

The machine learning approach that includes deep learning, builds intelligence from a vast number of corrective iterations. It is based on a self-correcting algorithm which is a continuous learning loop that gets more relevant and accurate the more it is implemented. NLP and AI-driven tools, when trained in the language of a specific business ecosystem, like asset management, can deliver valuable insights for every stakeholder across multiple software environments, and in appropriate fields.

Benefits of Using Magic DeepSight™ for Investment Research

1. Reduced personnel effort

Magic DeepSight™ extracts, processes, and delivers relevant data directly into your business applications, saving analysts’ time and enterprises’ capital.

2. Better decision-making

By freeing up upto 70% of the time invested in data extraction, tagging, and management, Magic DeepSight™ recasts the analysis process. It also supplements decision-making processes with ready insights. 

3. Improved data-accuracy

Magic DeepSight™ validates the data at source. In doing so, it prevents errors and inefficiencies from  creeping downstream to other systems. 

4. More revenue opportunities

With reduced manual workload and emergence of new insights, teams can focus on revenue generation and use the knowledge generated to build efficient and strategic frameworks. 

In Conclusion

Application of AI to the assiduous task of investment research can help analysts and portfolio managers assess metrics quickly, save time, energy and money and make better-informed decisions in due course. The time consumed by manual investment research, especially 10-K analysis, is a legacy problem for financial institutions. Coupled with emerging alternative data sources, such as ESG reports, investment research is more complicated today. After completing research, analysts are left with only a small percentage of their time for actual analysis and decision-making. 

A tool like Magic DeepSight™ facilitates the research process, improves predictions, investment decision-making, and creativity. It could effectively save about 46 hours of effort and speed up data extraction, tagging, and management by 70%. In doing so, it brings unique business value and supports better-informed investment decisions. However, despite AI’s transformative potential, relatively few investment professionals are currently using AI/big data techniques in their investment processes. While portfolio managers continue to rely on Excel and other necessary market data tools, the ability to harness AI’s untapped potential might just be the biggest differentiator for enterprises in the coming decade. 

To explore Magic DeepSight™ for your organization, write to us mail@magicfinserv.com or Request a Demo

The accessibility, accuracy, and wealth of data on the Securities and Exchange Commission’s EDGAR filing system make it an invaluable resource for investors, asset managers, and analysts alike. Cognitive technologies are changing the way financial institutions and individuals use data reservoirs like the SEC EDGAR. In a world that is being increasingly powered by data, artificial intelligence-based technologies for analytics and front-offices processes are barely optional anymore. Technology solutions are getting smarter, cheaper, and more accurate, implying that your team’s efforts can be directed towards high-value engagements and strategic implementations. 

DeepSight™ by Magic FinServ is a tailor-made solution for unstructured data-related challenges of the financial services industry. It uses cutting-edge technology to help you gain more accurate insights from unstructured and structured data, such as datasets from the EDGAR website, emails, contracts & documents, etc. saving over 70% of the existing costs.

AI-based solutions significantly enhance the ability to extract information and turn into knowledge from the massive data deluge, therefore providing enormous critical information to make decisions. This often translates to building higher competitiveness &, therefore, higher revenue.

What are the challenges of SEC’s EDGAR?

The SEC’s EDGAR presents vast amounts of data of public companies’ filed corporate documents, including quarterly and annual reports. While the reports are comprehensive and better accessible on public portals than before, filings such as daily filings and forms require much more diligent effort to peruse since it is tedious. There is also an increased margin of human error and bias when manually combing through data in such volumes. Quick availability of this public data also means that market competitors track and process it fast, in real-time. 

The numerous utilization possibilities of this data come with challenges in analysis and application. The issue of external data integration into fund management operations has been a legacy problem. The manual front-office processing of massive datasets is tedious and fragmented today but changing fast. Analysis of such large amounts of data is time-consuming and expensive; therefore, most analysts only utilize a handful of data points to guide their investment decisions, leaving untapped potential trapped in the other data points.  

After a lukewarm 1.1 percent organic net flow in the US every year between 2013 and 2018, cognitive technologies have now brought about a long-due intervention in the form of digital reinvention. Previously limited to applications in the IT industry, these technologies have been transforming capital management for a short while, but with remarkable impact. While their appearance in finance is novel, they present unique use cases to extract and manage data. 

How can technology help with the processing of EDGAR data used in the industry?

Data from EDGAR is being used across various business applications. Intelligent reporting, zero redundancies, and timely updates ultimately drive the quality of investment decisions. As investment decisions can be highly time-sensitive, especially during volatile economic conditions, extracting and tracking relevant information in real-time is crucial. 

Magic DeepSight™ is trained to extract relevant and precise information from SEC’s EDGAR, organize this data as per your requirements, deliver it in a Spreadsheet or via API’s or even better ingest it directly into your business applications. Since Magic DeepSight™ is built ground-up with AI technology, it has a built-in feedback loop, allowing you to train the system automatically with every use.

This focused information retrieval and precision analysis hastens and enhances the investment assessment process of a fund or an asset manager– a process that is fraught with tedious data analysis, complicated calculations, and bias when done solely manually.

Investment advice collaterals that are accurate, informative, and intelligible are part of the value derived through Magic DeepSight™. NLP and AI-driven tools, especially those trained in the language of your business ecosystem, can help you derive insights across multiple software environments in their appropriate fields. And all of it can be customized for the stakeholder in question. 

Meanwhile, tighter regulations on the market have also increased the costs of compliance. Technology offsets these costs with unerring and timely fulfillment of regulatory requirements. The SEC has company filings under the magnifying glass in recent exams, and hefty fines are being imposed on firms for not meeting the filing norms. Apart from pecuniary implications, fulfilling these requirements pertain to your firm’s health and the value perceived by your investors. 

What’s wrong with doing it manually?

Most of the front-office processes continue to be manual today, forcing front-office analysts slugging through large chunks of information to gain valuable insights. The information on EDGAR is structured uniformly, but the lengthy retrieval process negates the benefits of this organization of data. For example, if you wish to know acquisition-related information about a public company, you can access their Form S-4 and 8K filings easily on the SEC EDGAR website. But going through all the text to precisely find what is needed takes time. With Magic DeepSight™, you can automate this extraction process so analysts can focus on the next steps. 

And while a team of analysts is going through multiple datasets quickly, likely, relevant insights from the data that falls outside the few main parameters being considered are overlooked. And if such a hurdle arises with organized data, processing unstructured documents with large blocks of text, press releases, company websites, and Powerpoint presentations unquestionably takes much longer and is equally problematic. With Magic DeepSight™, you can overcome this blind spot. It can quickly process all values in a given dataset, and using NLP, it efficiently extracts meaningful information from unstructured data from multiple sources. Using this information, Magic DeepSight™ can provide you with new patterns and insights to complement your research team.

How does Magic DeepSight™ transform these processes?

While most data management solutions available in the market are industry-agnostic, Magic DeepSight™ is purpose-built for the financial domain enterprise. AI models, such as that of Magic DeepSight™ trained on financial markets’ datasets, can comprehend and extract the right data points. Built with an advanced domain-trained NLP engine, data is analyzed from an industry perspective and customized to your needs. Magic DeepSight™ is available on all cloud environments and on-premises if needed. Moreover, it integrates across your existing business applications without causing any disruptions to your current workflow.

DeepSight™ is built on a reliable stack of open source libraries, complimented by custom code, wherever needed, and trained to perfection by our team. This versatility is also what makes it easily scalable. Magic DeepSight™ can treat a wide range of information formats and select the most appropriate library for any dataset. By using Magic DeepSight™, Search—Download–Extraction of relevant information from the SEC EDGAR database can become easy and efficient. Information on forms such as disclosures on a 10K, including risk assessment, governance, conflict of interest, etc. is accurately summarized in a fraction of the time taken previously, freeing up space for faster and better-informed decision making. 

But it is more than just a data extraction tool. DeepSight™ is also integrated with other technologies such as RPA, smart contracts, workflow automation, and more– making it an end-to-end solution that adds value to each step of your business processes. 

Our team can also customize DeepSight™ to your enterprise’s requirements delivering you automated, standardized, and optimized information-driven processes across front-to-back offices.

What business value does Magic DeepSight™ provide?

• It completely automates the process of wading through vast amounts of data to extract meaningful insights saving personnel time and effort, thus reducing costs up to 70%.
• It becomes an asset to the research processes by employing NLP to extract meaningful information from an assortment of unstructured document types and formats, improving your firm’s overall data reservoir quality.
• The band of your insights, made possible with AI, offer a richer perspective that was previously hidden, thus helping you drive higher revenues with better-informed investment decisions. 

Magic DeepSight™ digitally transforms your overall operations. Firms that adopt AI, data, and analytics will be better suited to optimize their business applications. 

To explore Magic DeepSight™ for your organization, write out to us mail@magicfinserv.com

COVID 19 and the associated social distancing and quarantine restrictions, has dictated new measures for business standards, forcing companies into a major overhaul in the way they work. Remote Working is just one key part of this change, this impacts workplaces and the global workforce significantly.

This cause-effect relationship is now at the forefront, fundamentally transforming existing business models, business practices, business processes, and supporting structures and technology. According to Gartner, “CIOs can play a key role in this process since digital technologies and capabilities influence every aspect of business models.”

Business process management ( BPM)  was the primary means for investment banks and hedge funds  to make internal workflows efficient. In investment banking, BPM  focused on the automation of operations management by identifying, modeling, analyzing, and subsequently improving business processes.

Most investment firms have some form of BPM for various processes. For instance, compliance processes appear to have some form of software automation in their workflows at most investment banks and hedge funds. This is because banking functions such as compliance, fraud, and risk management exert pressure to develop cost-effective processes. Wherever Automation was not possible,  manual labor-intensive functions were outsourced through KPO’s to comparatively cheaper South-East Asian destinations thereby reducing costs. With COVID-19’s social distancing norms levied, this traditional KPO model to handle Front, Middle, and Back Office processes is cracking up as it relies on several people working together. There is an urgent need to rethink these processes with a fresh vision and build intelligent systems that are remotely accessible, for handling all such processes like KYC, AML, Document Digitization, Data Extraction from Unstructured documents, Contract Management, Trade reconciliation Invoice Processing, Corporate Actions, etc.

Now more than ever, organizations need to embrace agility, flexibility, and transformation. As per KPMG, the modern enterprise must become agile and resilient to master disruption and maintain momentum. Optimizing the operations process can transform the business to support lean initiatives that lead to innovation—an aspect that can no longer be ignored. With the help of cross-functional domain experts, organizations can discover and subsequently eliminate inefficiencies in the operations and business processes by identifying the inconsistencies, redundancies, and gaps that can be streamlined.  Intelligent Workflow initiatives and goals align business improvement with business objectives and visibly reduce the probability of negative ROI and impact on projects and initiatives.

Using new technologies like AI and Machine Learning, organizations can quickly adapt and improve with precision and gain the multi-layered visibility needed to drive change and reach strategic goals across an enterprise. The proper use of Artificial Intelligence can solve business case problems and relieve enterprises from various technology or data chokes. AI techniques can help traditional software perform tasks better over time, thus empowering people to focus their time on complex and highly strategic tasks.

Best-Practices for Adoption of AI-Based BPM Solutions

Before moving into AI-based process automation, a crucial idea for investment banking business leaders to realize is that they need to shift their perspective of emerging technology opportunities. Many AI projects will be deployed before they return the desired result, 100 % of the time.

AI ventures require ample algorithmic tuning, so it can take several months to reach a state of high precision and confidence. This is important because banks, in their core business processes, cannot jump into large AI projects and expect seamless functions across the board straightaway. Any large project would result in a temporary impediment to the specific business process or push it into a downtime before the AI project is complete. 

So bankers need to develop a mentality of try-test-learn-improve while considering AI to gain confidence in data science projects. Also, it is advisable to choose an AI service provider with extensive experience and knowledge of the domain, to achieve desired results. An investment firm should expect to have a prototype solution in the first iteration which they need to improve by incorporating user feedback to correct minor issues to achieve an MVP status. The smaller and shorter projects, that focus on improving a particular sub-process within the entire process workflow are better suited for investment firms. This approach allows small AI teams to develop and deploy projects much faster. Such projects are advisable since they bring a significant positive business impact, while still not hindering the current workflow and process.  

Such attitudinal changes are decisive shifts from the conventional approach to technology that investment banking leaders have taken. This is presumably not something firms can change overnight and requires careful preparation, planning, and a strategy to help the workforce have an incremental improvement approach to business processes. These fundamental shifts demand that leaders prepare, motivate, and equip their workforce to make a change. But leaders must first be prepared themselves before inculcating this approach in their organizations.

Our interactions with CXO’s in the investment banking industry indicate that process optimization applications of AI can bring a  disproportionate benefit in terms of operational efficiency,  sorely needed in these challenging times.  

Magic FinServ offers focussed process optimization solutions for the Financial Services Industry leveraging New Gen Technology such as AI, ML, across hedge funds, asset management, and Fintechs. This allows financial services institutions to translate business strategy and operational objectives into successful enterprise-level changes, thus positively impacting revenues and bottom-line growth. With the relevant domain knowledge of capital markets and technological prowess, our agile team builds customized turnkey solutions that can be deployed quickly and demonstrate returns as early as 2 weeks from the first deployment. Discover the transformation possibilities with our experts on AI solutions for hedge funds and asset managers. 

Write to us mail@magicfinserv.com to book a consultation.

Contracting as an activity has been around, ever since the start of the service economy. But despite it being a well-used practice, very few companies have mastered the art of managing contracts efficiently or effectively.  According to a KPMG report, inefficient contracting leads to a loss of 5% to 40% of the value of a given deal in some cases. 

The main challenge facing companies in the financial services industry is the sheer volume of contracts that they have to keep track of; these contracts often lack uniformity and are hard to organize, maintain and update on a regular basis. Manual maintenance of contracts is not only difficult but also cumbersome and prone to multiple forms of errors.  Also, it poses the risk of missing important deadlines or missed scheduled follow-ups, as written in the contract and could potentially lead to expensive repercussions.

Contract management is a way to manage multifarious contracts from vendors, partners, customers, etc. so that data from these contracts can be easily identified, segregated, labeled and extracted to be used in various cases and also updated regularly. 

Recent technological advances in Artificial Intelligence (AI) and Machine Learning, are now helping companies resolve many of the contracting challenges by delivering efficient contract management as a seamless automated solution. 

Benefits of Using AI in the contract management lifecycle

Basic Search

AI can help in enhancing the searchability of the contracts including clauses, dates, notes, comments and, even metadata associated with it. The AI method used for this purpose is called natural language processing(NLP) and the extraction of metadata is done at a granular level to enable the user to search from the vast repository of contracts in an effective manner.

Example: This search function would be extremely useful for the relationship managers/chat-bots to answer any customer queries pertaining to a particular contract. 

Analysis and Diagnostic Search:  AI can be used to proactively identify expiry dates, renewal dates, follow-up dates or low KPI compliance, and then can be used to apply suggestive course of action or flag any alerts. Analytics can further be used to study and predict any kind of risks or non-compliance and therefore send a notification to relevant stakeholders for pending payments or negotiations.

Example: This can be effectively utilised for improving customer satisfaction as well as guide negotiations based on accessible information.

Cognitive Support: AI is highly sought for its predictive intelligence. AI’s predictive capabilities can be used to do an analysis of the existing contracts to understand contract terms & clauses. Its pattern recognition algorithms can identify points of parity, differentiations on pricing, geographic, products & services. Based on the predictive analysis, AI can provide suggestions for inclusion/exclusion of clauses, terms & conditions, etc when authoring new contracts. 

Example : AI systems may automatically predict and suggest clauses pertaining to NDA (non-disclosure agreement) based on the historical contracts that have been previously processed and the events associated with it.

Dynamic Contracts: Advanced AI can be used to build an adaptive dynamic contract. Based on the past data and by taking into account external factors such as market fluctuations, currency exchange, prices, labor rate, changes in laws and regulations, etc, AI algorithms can create a contract. Such a contract would require auditing by an expert but nonetheless would reduce the effort required to generate the contract.

Example: AI can be used to assess existing contracts for making them GDPR (General Data Protection Regulation) compliant. It will insert the relevant data privacy terms and conditions into the contract and subsequently notify the concerned stakeholder about the changes in the contract, so they can be verified.

Challenges in contract management with AI-ML

The use of AI and Machine Learning for contract management is highly promising but it is also challenged by few limitations. 

Machine Learning (ML) is only as effective as the training data that has been used to train the ML algorithms. Therefore, before any AI-ML application is put into practice, an exhaustive dataset of contracts must be developed and then classified, sorted, labeled, and retrieved based on the metadata. This would provide the base, as training data, for AI to build up and therefore put the ‘intelligence’ in the Contract Management process.

For the exhaustive dataset to be developed, all the contract data must be assimilated together. In many organizations, the contracts are still hard copies lying in cabinets. Approximately 10% of written agreements aren’t even traceable. Even when digitized contracts are available, for the AI machine to read these contract’s insights, they must first be in uniform contents. This not only requires scanning of all the documents but also the ability to extract the meaning of the content in the contracts. 

Overcoming the challenges

In order to make the contract portfolios AI-ready, the first step is to  digitize these contract documents. This can be done using OCR (optical character recognition). OCR reads the physical document as a human eye would read it and converts into digitized text which can easily be searched with ML formulas. While it may be too onerous to scan all historical contracts, this purpose can be accomplished by using a CMS (contract management software), which is capable of converting the documents into machine readable filed, thus making a significant data pool. Then AI, can be used to use this data to gain relevant insights. When AI algorithms access huge pools of data, its ability to decipher patterns and provide insights becomes much stronger. The predictive insights can be achieved by incorporating NLP (natural language processing). NLP allows contact groups to identify when contracts have deviated from defined standards. This makes the approval process, negotiation process much faster when the stakeholder is aware of the current contract version deviation from standards. NLP is also used in reporting risk based on language meaning rather than just string matching. For example, identifying those contracts which are about to expire and starting their renewal process.

Conclusion

Potentially, AI in contract management will change the contract management lifecycle to uplevel the strategic role of the contract managers, which would position them in a superior spot while negotiating terms of contracts. It can also help tremendously in strategic planning, risk management, supplier search, and final selections. Thus enhancing the efficiency and effectiveness of category managers. AI innovation continues to play a vital role when contract managers educate themselves and ensure that their contract processes are fully digitized and AI-ready.

Get started with Artificial Intelligence by booking a workshop with us today!