Back to InsightsIf data is the proverbial “lifeblood” of investment decisions, the sheer volume and complexity of information are draining the energy from investment professionals.
Enter machine learning (ML) and artificial intelligence (AI). These powerful technologies are revolutionizing how firms ingest, normalize, and understand data, and simplifying how teams work with data.
As data grows in size and complexity, an enterprise may need to handle hundreds — or even thousands – of diverse sources. Whether firms are using structured, semi-structured, or unstructured information via real-time feeds, flat files, or streaming sources, they must prepare to evaluate and potentially incorporate new information.
ML models are helping firms intelligently manage mounting volumes of data and quickly access ready-to-use information. At a high level, ML delivers algorithms to ingest data, identify patterns, and make decisions. ML thrives on new information and repetition to learn and improve predictability and performance. As a result, higher data quality drives more accurate output.
From Data to Action
Using data science to generate insights and optimize operations is growing in lockstep with the industry’s acceleration of data and sources. By applying machine learning across the ingestion lifecycle, firms achieve efficiency, eliminate redundant tasks, and better manage their data pipelines.
Take market vendor integrations as an example. Firms often build adapters as a common integration point to source data from financial feeds and support complex transactions. To automate manual, time-consuming processes, natural language processing (NLP) models use standard catalogs defined by market vendors to read input sources and auto-detect schemas and mappings. These advanced solutions can manage corporate actions, internal modeling of securities, and more. Similar solutions apply to semi-structured datasets, such as SWIFT message tags or schema definitions for XML feeds. Using metadata-driven standardizations, NLP tools enable firms to maintain high data standards and quickly deliver information to end users.
Beyond Declarative Data Checks
Data from counterparties and broker sources, such as pricing, can be a valuable asset in helping firms analyze industry sectors, geographic regions, credit ratings, and risk.
However, challenges often arise in manually mapping counterparty feeds due to various sources and disparate systems. ML enhances automation for firms that require extensive custom code to implement quality checks and validation. As a machine performs more transformations, it studies and recommends conversions based on how it historically ingested information. In addition to quicker insights, a machine can suggest mappings for counterparty, vendor, and administrator files based on how it maps, cleanses, formats, and prepares similar flat files for ingestion. Instead of manually identifying and updating each normalization, analysts can focus on reviewing the machine’s recommendations and other higher-value activities. As firms scale across counterparties and map more broker statements, the consistent flow of information improves the model’s accuracy.
Procedural, or rules-based transformations, which follow the traditional extract, transform, and load approach, also enable quicker analysis. Under this process, business teams provide reporting requirements to the owners, who then build, design, and implement the pipeline before delivering the final data. In addition, ML models can streamline operations with suggestions that allow analysts to implement changes faster and reduce turnaround time for data ingestion and transformation.
Adding Structure to Unstructured Data
Whether firms are invested in the public or private markets – or converging their investment focus – managing unstructured data is one of their biggest challenges. Deal information in lengthy, complex legal documents, quarterly reports, and capital account statements is loaded with bespoke terms.
In fact, it’s widely estimated that 80% to 90% of the world’s data is unstructured.[1] While ingesting low-volume data can be done manually, large volumes of information become more complicated.
Sophisticated techniques, such as optical character recognition (OCR), are a common method of digitizing sources, including PDFs and textual data. Firms also use OCR in processes such as machine translation, extracted text-to-speech, and text mining to systematically identify patterns and commonly sourced data points. These tools allow organizations to automate how they capture, extract, and validate unstructured workflow before storing data in a warehouse. The tool’s flexible nature allows users to add new sources and data points or make schema changes to existing workflows. A solution that can automatically scan a landing area, ingest new sources, and adapt to schema changes is a must-have.
Responding to the Burst of Data
With the explosion of data, businesses must continue investing in smart capabilities to better anticipate key business and industry events. From data cleansing and preparation to analytics, storage, and retrieval, machine learning is helping investment firms intelligently manage their data.
A key challenge will be continuously feeding machines with high-quality data that enables them to learn and create transformative outcomes. As machines take in more information, they better understand how to build user trust in their accuracy and reliability. Continuous innovation and intelligent use of data science will be an evolving, critical component in creating a low-touch, data-driven ecosystem.
[1] Tapping the Power of Unstructured Data, MIT Management Sloan School
Authors:
Ram Chidambaram, Senior Vice President, Product Management, Arcesium
Rahul Bighane, Product Manager, Arcesium
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