Data about who owes how much to whom is at the core of any bank\u2019s business. At Bank of New York Mellon, that focus on data shows up in the org chart too. Chief Data Officer Eric Hirschhorn reports directly to the bank\u2019s CIO and head of engineering, Bridget Engle, who also oversees CIOs for each of the bank\u2019s business lines.\n\n\u201cIt\u2019s very purposeful because a lot of the opportunities for us around data require tight integration with our technology,\u201d says Hirschhorn. \u201cI\u2019m a peer to the divisional CIOs of the firm, and we work hand-in-glove because you can\u2019t separate it out: I can make a policy, but that alone doesn't get the job done.\u201d\n\nHirschhorn, who joined the bank in late 2020, has worked in financial services for over three decades, during which the finance industry\u2019s concerns about data have changed significantly.\n\n\u201cTwenty years ago, we were trying to make sure our systems didn\u2019t fall over,\u201d he says. \u201cTen years ago, we were worried about systemic importance, and contagion. When you solve some of the more structural concerns, it all gets back to the data. We are incredibly bullish on building advanced capabilities to understand the interconnectedness of the world around us from a data perspective.\u201d\n\nOne key to that endeavor is being able to identify all the data related to an individual customer, and to identify the relationships that link that customer with others. Banks have a regulatory requirement to know who they\u2019re dealing with \u2014 often referred to as KYC or \u201cknow your customer\u201d \u2014 to meet anti-money-laundering and other obligations.\n\n\u201cThe initial problem we were looking to solve is a long-standing issue in financial markets and regulated industries with large datasets,\u201d Hirschhorn says, \u201cand that was really around entity resolution or record disambiguation,\u201d or identifying and linking records that refer to the same customer.\n\nBeing able to identify which of many loans have been made to the same person or company is also important for banks to manage their risk exposure. The problem is not unique to banks, as a wide range of companies can benefit from better understanding their exposure to individual suppliers or customers.\n\nDefining a customer with data\n\nBut to know your customers, you must first define what exactly constitutes a customer. \u201cWe took a very methodical view,\u201d says Hirschhorn. \u201cWe went through the enterprise and asked, \u2018What is a customer?\u2019\u201d\n\nInitially, there were differences between divisions about the number of fields and type of data needed to define a customer, but they ended up agreeing on a common policy.\n\nRecognizing that divisions already had their own spending priorities, the bank set aside a central budget that each division could draw on to hire developers to ensure they all had the resources to implement this customer master. The message was, \u201cYou hire the developers and we will pay for them to get on with it,\u201d Hirschhorn says.\n\nWith the work of harmonizing customer definitions out of the way, the bank could focus on eliminating duplicates. If it has a hundred records for a John Doe, for example, then it needs to figure out, based on tax ID numbers, addresses, and other data, which of those relate to the same person and how many different John Does there really are.\n\nBNY Mellon wasn\u2019t starting from scratch. \u201cWe actually had built some pretty sophisticated software ourselves to disambiguate our own customer database,\u201d he says. There was some automation around the process, but the software still required manual intervention to resolve some cases, and the bank needed something better.\n\nImproving the in-house solution would have been time consuming, he says. \u201cIt wasn't a core capability, and we found smarter people in the market.\u201d\n\nAmong those people were the team at Quantexa, a British software developer that uses machine learning and multiple public data sources to enhance the entity resolution process.\n\nThe vendor delivered an initial proof of concept to BNY Mellon just before Hirschhorn joined, so one of his first steps was to move on to a month-long proof of value, providing the vendor with an existing dataset to see how its performance compared with that of the in-house tool.\n\nThe result was a greater number of records flagged as potentially relating to the same people \u2014 and a higher proportion of them resolved automatically.\n\n\u201cThere\u2019s a level of confidence when you do correlations like this, and we were looking for high confidence because we wanted to drive automation of certain things,\u201d he says.\n\nAfter taking some time to set up the infrastructure and sort out the data workflow for a full deployment, BNY Mellon then moved on to a full implementation, which involved staff from the software developer and three groups at the bank: the technology team, the data subject matter experts, and the KYC center of excellence. \u201cThey\u2019re the ones with the opportunity to make sure we do this well from a regulatory perspective,\u201d he says.\n\nQuantexa\u2019s software platform doesn\u2019t just do entity resolution: It can also map networks of connections in the data \u2014 who trades with whom, who shares an address, and so on.\n\nThe challenge, for now, may be in knowing when to stop. \u201cYou correlate customer records with external data sources, and then you say, let\u2019s correlate that with our own activity, and let\u2019s add transaction monitoring and sanctions,\u201d he says. \u201cWe\u2019re now doing a proof of concept to add more datasets to the complex, as once you start getting the value of correlating these data sets, you think of more outcomes that can be driven. I just want to throw every use case in.\u201d\n\nInvesting in technology suppliers\n\nBNY Mellon isn\u2019t just a customer of Quantexa, it\u2019s also one of its investors. It first took a stake in September 2021, after working with the company for a year.\n\n\u201cWe wanted to have input in how products developed, and we wanted to be on the advisory board,\u201d says Hirschhorn.\n\nThe investment in Quantexa isn\u2019t an isolated phenomenon. Among the bank\u2019s other technology suppliers it has invested in are specialist portfolio management tools Optimal Asset Management, BondIT, and Conquest Planning; low-code application development platform Genesis Global; and, in April 2023, IT asset management platform Entrio.\n\nThe roles of customer and investor don\u2019t always go together, though. \u201cWe don\u2019t think this strategy is applicable to every new technology company we use,\u201d he says.\n\nWhile some companies may buy a stake in a key supplier to stop competitors taking advantage of it, that\u2019s not BNY\u2019s goal with its investment in Quantexa\u2019s entity resolution technology, Hirschhorn says.\n\n\u201cThis isn\u2019t proprietary; we need everybody to be great at this,\u201d he says. \u201cPeople are getting more sophisticated in how they perpetrate financial crimes. Keeping pace, and helping the industry keep pace, is really important to the health of the financial markets.\u201d \n\nSo when Quantexa sought new investment in April 2023, BNY Mellon was there again\u2014this time joined by two other banks: ABN AMRO and HSBC.