Authored By
Rahul Bighane
Rahul brings over 8 years of product management expertise to Arcesium, tackling complex technical and functional challenges. His passion lies in untangling complex data, regulatory, and functional issues within product areas like Platform Integration and Treasury.
Share This post
If data is the proverbial “lifeblood” of investment decisions, the sheer volume and complexity of information is 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.
The use of data science to generate insights and optimize operations is growing in lockstep with the industry’s explosion in sources and uses of data from both traditional and alternative 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 source corporate actions data, internal modeling of securities, and more. Similar solutions apply to semi-structured datasets, such as 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.
YOU MAY ENJOY: In AI We Trust?
Data from counterparties and vendor sources, such as pricing, can be valuable in helping firms analyze industry sectors, geographic regions, credit ratings, and risk. Consider the benefits of enabling your technology to evaluate the ingestion and transformation process:
Whether firms are invested in the public or private markets — or a convergence of both — managing unstructured data can be a major challenge. Hedge funds and institutional investment firms increasingly rely on unstructured data from social media and new stories, and some have even gone as granular as looking at satellite imagery of parking lots at large department stores, to get a pulse on trends. Specific to the private markets, deal information in lengthy, complex legal documents, quarterly reports, and capital account statements is loaded with bespoke terms.
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, like optical character recognition (OCR), commonly help digitize sources, such as PDFs and textual data. Firms also use OCR in processes such as machine translation, speech synthesizers commonly known as 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 tools’ flexible nature allows users to add new sources and data points or make 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.
RELATED READING: AI Built for Success Begins With Data That’s Ready for AI
For years, a common industry challenge has been the time spent on mapping market data and integrating counterparty feeds. Tools that extract, transform, and load data, commonly called ETL tools, allow firms to use advanced technologies for data ingress and feed downstream services – freeing up their teams to focus on other priorities. With the application of ML, mapping data becomes more automated through continuous feedback and improvements to the model.
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.
Sources:
1. Tapping the Power of Unstructured Data, MIT Management Sloan School
No spam. Just the latest releases and tips, interesting articles, and exclusive interviews in your inbox every week.
No spam. Just the latest releases and tips, interesting articles, and exclusive interviews in your inbox every week.
