Machine to machine reporting for the Digital Era

The amount of data modern life produces is almost unfathomable - an estimated 2.5 quintillion bytes of data are produced by humans every day. That volume of data can no longer be processed manually by humans - we simply don’t have the capacity.  

This is especially true in the world of financial supervision. The 2008 financial crisis wiped out soft-touch regulation. Financial information is coming under ever-increasing scrutiny, and the pool of regulated entities is diversifying and expanding.  

For financial regulators, the volume of data has increased exponentially. A tipping point has arrived where regulators must be freed from the manual handling of data so that they can focus on supervision. 

Fortunately, we have the technologies to facilitate the efficient transfer and analysis of this data. Solutions like Regulatory Data Management, machine learning, Artificial Intelligence (AI), and Machine to Machine reporting (M2M) can prevent regulators from becoming overwhelmed. 

Impact of machine to machine reporting 

The role of the regulator must evolve: In the past, the regulator was a partial administrator or data steward as well as a supervisor. For a sustainable future, machine to machine must allow the regulator to take on a purely supervisory role.  

The world is changing, and regulators need to move with the times to stay ahead of the tide or be swept aside by it. Otherwise, there is a risk that humans will be working solely on data stewarding activities with little time left for actual supervision.  

Regulators will try to mitigate the problem by increasing the headcount but inevitably the tidal wave of data will overwhelm human efforts. Unless something changes, the practice of regulation will become unmanageable - risking market sustainability. 

“To keep up with the increasing competition from firms inside and outside the industry, banks should provide innovative services at the same rate as other smaller, leaner organizations do… Open APIs and open platform banking are set to change the shape of financial services completely.” Accenture.com (2019) 

The widespread adoption of Machine to Machine (M2M) reporting is likely to become part of the new norm.  

How does machine to machine reporting support regulators?  

Machine to Machine reporting is part of a set of modern technologies and strategies that are used to ensure state-of-the-art regulatory efficiency for the modern age. The long-term vision for M2M and AI is that algorithms can detect deviations from patterns so that potential crises can be forecast in advance. 

This is a rapidly evolving space, and some other concepts we may expect to see developed in the coming years include:

• Advanced AI techniques applied to find more anomalies in data and detect them sooner
• The industry will advance towards a Global Data Model so that the worlds’ data can be standardised per industry
• Cross Border Regulation will become more prominent
• Any solution would require a combination of strategies and technologies to be effective. No single approach should be considered a panacea. Each concept builds on earlier ones to create a powerful solutions portfolio.

The role of the regulator is to determine and provide guidance on the most suitable technologies and strategies for each use case.

Benefits of machine to machine reporting for regulators

1. Reduces Human Effort
Machine to machine Reporting can allow data to be processed, validated, and analysed without human interaction.

Humans no longer need to do the cumbersome and distracting work of scrubbing large amounts of data and moving it around. Rules or alerts are set up that call for human supervision only when certain triggers get invoked.

 Their time is freed up to supervise and only intervene where warranted.

 2. Reduces Human Error
There is more data than ever before. This means that the risk of human error is greater than ever before. machine to machine reporting can mitigate this threat, as the data can be pre-configured with the appropriate level of validation and rules already in place.

Once a machine to machine reporting solution has been delivered and effectively tested it does not make mistakes no matter how much data it processes. If a bug has been introduced that leads to inaccurate supervision then this bug only has to be fixed once. If a human makes a mistake, there’s no guarantee that they won’t make it a second or third time.

3. Increased Timeliness of Data
When humans are the main administrators and couriers of data, it takes time to compile and submit. This means that the data may already be stale by the time the regulator gets around to viewing it, and all the human effort will have amounted to is low-quality data.

If the task of moving this data from a regulated entity to the regulator and beyond is delegated to machines then it can be moved at up to real-time speeds. This means that the data will be fresh and relevant. There’ll be less back and forth between parties and the chances of “right-first-time” submissions will increase greatly.

Machine to machine case studies – ‘Push’ and ‘pull’ methods in action

NATIONAL BANK OF RWANDA

As part of an enterprising dedication to financial inclusion, the National Bank of Rwanda has developed an electronic data warehouse (EDW) system. The system is designed to ‘pull’ data from eight banks, three microfinance institutions, two money transfer operators, and one mobile network operator regularly. This system has proved successful and generated a lot of interest globally.

BANK OF GHANA

The Bank of Ghana worked with us to roll out APIs that facilitate financial institutions in pushing submissions to the regulator.

They made API submission an option for all of their 41 reporting obligations. Initially, they see their 23 banks as the main consumer of the APIs but they are committed to onboarding hundreds of smaller institutions too.

Their ultimate aim is that absolutely everything travels through API and human interaction is kept to a minimum. The Bank of Ghana is one of the first regulators globally to achieve this level of adoption.

The implementations adopted by Rwanda and Ghana are very effective solutions for their respective challenges. The approaches are very similar, but there is one key difference. Rwanda pulls data from each financial institution’s API while Ghana provides their own API and the financial institutions push to it.

These opposing yet strategically similar concepts of ‘push’ and ‘pull’ are at the heart of the current interest in data on demand.

Within the regulatory field, we find that the institution pushing to the regulator is often the most suitable. As a machine to machine solution, it is equally effective in terms of real-time data collection.

Evolution of machine to machine reporting solutions

We see machine to machine solutions evolving in the future as regulators begin stipulating the data structure that financial institutions should adopt. If financial institutions are mandated to store their data in the same structure as that recommended by the regulator then the doors will be opened to standardised data.

This will mean that all the financial institutions are speaking the same language and we will see alignment across the industry. Generic shared solutions can be rolled out with a greater economy of scale. This will enable RegTech innovation and reduce regulatory burden compared to each financial institution making parallel individual efforts.

API Connectors

We have created a new component within our software stack known as the API Connector. It removes the need to create bespoke software applications for each API service request.

The API Connector facilitates both the pulling of data into our system from an External API and the pushing of data from our system to an External API. It’s compatible with modern API standards, and it is highly configurable so it can be used to connect to any number of different APIs. 

Our clients are already taking advantage of this functionality for use cases such as pushing data into their Hadoop system and pulling financial institution information from their native MDM system. 

Final thoughts on machine to machine reporting

The age of data is upon us. Between the 2008 financial crisis and now, the use cases for machine to machine reporting have expanded dramatically.

It is in the interest of regulators to adapt before the data volumes become overwhelming.

Regulators need to choose the optimal solution from the powerful portfolio of options that are now available. We believe machine to machine reporting will become a vital cog in the data processing engine.

It is no longer achievable for humans to manage regulatory data alone. Now, more than ever, it is vital that machines deal with the processing of data so that humans can be freed up to become full-time regulators.