Protecting the bottom line

With data volume exploding, treasurers must take on expanded responsibility as first line of defense. Do your teams have the skills to guard the bottom line?

 

6 minute read

Key takeaways

  • The explosion of data calls for treasurers to take on more responsibility as first line of defense. Sophisticated tools help level up analysis of structured and unstructured data to identify fraud patterns and lead to new opportunities to monitor, control and reduce errors.
  • Helpful tips to managing data range from embracing tech, optimizing existing tools to building strong IT partnerships.
  • As data analytics becomes more sophisticated, it is moving from the sole jurisdiction of IT departments to a wider issue demanding that treasurers become versed in data science and data quality management.

How treasurers are becoming data protectors

 

A data mountain that grows every day means more opportunities for cyber criminals. How do you manage your risk? With intelligent data management processes and technologies and investment in new skills.

 

Using data to protect your data

 

For treasurers, the data explosion means increasing availability of different types of data, both structured and unstructured, plus more sophisticated tools for dealing with it. Technologies like artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) are now widely used to find patterns that point to potential errors and fraud. These innovations have been supported by improvements in computing and data engineering over the last few years.

 

Tech innovation grabs the headlines, but we’re finding that new ways to use existing tools are also playing their part. Joan Gelpi, Head of Data & AI of Bank of America’s Global Transaction Services, notes that, “Clients are using APIs to link treasury systems with managed bank accounts data, ensuring a more real-time reflection of transactions and authorized individuals.” Similarly, regression analysis is being used to spot fraudulent commercial card or wire payments, and Natural Language Processing (NLP) is allowing treasurers to process non-structured data more effectively. Treasurers are also adopting tech from other business areas, including data science tools and data visualization tools. These can be useful in helping monitor real-time processes and flows of data through the organization, allowing for more proactive methods of fraud prevention and risk management.

 

While harnessing technology is undoubtedly important, companies should not overlook process management and structured workflows. AI and machine learning can be helpful, but processing data on a real-time basis oſten adds more in terms of fraud prevention. More companies are now continuously monitoring data, not only to look for fraud patterns but to apply controls that reduce errors.

Fraud-preventing tech

  • Artificial intelligence
  • Robotics
  • Natural language processing
  • Smart contracts
  • Machine learning

Treasury departments need to become data scientists

 

As data analytics becomes more sophisticated, data is shiſting from being the sole remit of IT departments to becoming a wider issue that treasurers need to have a hand in to guard the bottom line. But treasury departments that are large enough to act as data scientists need to upgrade their skills in two key areas: data science and data quality management. For newly hired data scientists to fully support treasury in its fight against fraud, subject-matter expertise is needed. We’re starting to see staff with treasury backgrounds being hired, and corporate leadership in many organizations is supportive of this shiſt. But that’s not all that’s required. Data quality management also needs to be a key area of focus, with people dedicated to cleaning up data that’s incomplete or erroneous. This means hiring treasury staff with data literacy, technology and data architecture backgrounds.

“Treasury departments that are large enough to act as data scientists need to upgrade their skills in two key areas: data science and data quality management.”

Partnering with IT is key

 

Given the need for specialist skills, one of the things companies need to as is, “How and where am I going to manage my data?” Taking the IT route may require a change in mindset. Traditional IT departments tend to be built around applications, with siloed teams to manage each one, meaning that they’re not well equipped to work across applications. But the skills required by today’s treasury departments involve bringing together data sets, cross-referencing them and moving and moving quickly to identify trends. It makes sense for organizations to invest more in IT so that they can develop the data architecture and data engineering skills they need. IT and treasury are beginning to partner more closely on this, but there’s still work to be done. 

Tips for managing data

Establish a good data quality program:

Define what’s important, how data flows and what could corrupt it.

Embrace tech:

Make the most of innovations like AI, ML and NLP

Optimize existing tools:

Find new ways to use APIs, TMSs and OCR.

Hire data scientists in the treasury department:

Start using their skills immediately.

Partner with IT:

Request data engineering and data protection skills. 

Learn from IT and shared service departments:

Leverage their knowledge as drivers of automation.

Prioritize your data:

Decide what you want to work on and monitor.

Put your plans into practice:

Get into a cycle of continuous improvement. 

Be persistent:

You’ll see a step change in your ability to detect fraud risks.

What can treasurers do to help?

 

Gelpi’s advice is, “Go to your IT and shared service departments and learn from them, because they are driving the use of data and automation.” Treasurers can also request extra investment and data-related skillsets that will ultimately benefit the entire business. They need to think about data standards, and how to work with IT to onboard people with the right skills. At present, many treasury departments are early in the process of bringing data scientists in.

 

Looking ahead

 

Organizations will likely need to make larger investments to combat ever more sophisticated fraud. We’re seeing identity resolution, or authentication, become a key area of investment. Additionally, companies are increasingly working with each other, potentially making the whole process safer. There’s a willingness to share information—when a payment goes through, it’s now possible to check that same vendor, based on payments to other organizations. Gelpi notes, “One thing is certain: Data is quickly changing treasury departments, and they’re ramping up to meet the challenge.”

Joan Gelpi | Head of Data & AI, Global Transaction Services, Bank of America

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