RegTech is nothing new. In fact, it has been around for many years through ‘trade surveillance’ and other AML capabilities that are solely focused on mitigating a specific risk providing scalable solutions and standardisation. However, if you’re developing a solution as a FinTech or considering a new capability as a bank, have you considered the question ‘is this solution more “Tech” than “Reg” or has it achieved the right balance of both ?’

  • Applying technology [e.g. ‘machine learning’ or even more-advanced types of artificial intelligence (AI)] is appropriate if: a) there is a clear link to mitigating a risk, b) it does so efficiently, c) demonstrates measurable impact, and d) aligns to the bank’s strategy and risk appetite;
  • Globally, a new piece of regulation is published approximately every 7 minutes, so there must be plenty of scope to develop the right solutions. However, a common perception among banks is that only a small percentage of RegTech solutions address meaningful regulations or there’s a lack of compelling business case, especially if the ‘RegTech’ balance is wrong;
  • RegTech firms have made the most-efficient IT processes such as Agile and DevOps, but speed to market without adequate testing or ‘code verification’ can lead to new IT issues arising compared to tackling the primary risk is was intended to mitigate;
  • Surprisingly, Banks still have a propensity to ignore RegTech solutions and continue to work on ‘patching-up’ or changing their legacy IT systems leaning toward ‘Tech’ rather than ‘RegTech’.
  • Even with appropriate and well-designed RegTech solutions, their credibility and effectiveness is only as good as the data that flows through them.

However, the good news is that RegTech has a major part to play in safeguarding a customer’s trust in financial services. It also has a role to play in keeping criminals at bay whom already exploit technology leading to new types of risk. Understandably, Risk and Compliance Departments can only grow to a certain size that is proportionate with the business and budget available, so ‘appropriate’, scalable solutions will be required to tackle a variety of growing risks and challenges: In particular:

Financial crime

(Real-time) payments monitoring, reporting, blocking through machine learning and predictive analytics for markets trade surveillance. Real-time margins calculations, risk management engines, compliance monitoring, end-of-day reconciliation of all transactions, and reporting for all types of trades and transactions;


Cryptography, itemised security and information sharing technology and potentially distributed ledger (blockchain) for improving data management, security and aggregation between institutions and with regulators;

Regulatory reporting

Cognitive computing / deep learning techniques that enable “regulatory radar” software with understanding of regulations, along with opportunities to innovate reporting formats and standardization, including automated interfaces with regulators;

Risk Data

Aggregation and management – Advanced analytics and new types of models can improve modelling and data analysis. Open platforms and networks can help rationalise and build a robust standard data dictionary across banks;

Identity verification

The blockchain is already used as a mechanism for digital identity verification and may develop in the future into a secure information sharing system. Increasingly, there is more use of biometric, social verification, or other new means of identity verification, especially in emerging markets, and the need to comply with national e-identity, e-certificate and e-signature systems;

Know your customer (KYC)

Data mining, natural language processing, and visual analytics for the processing and analysing of unstructured data may be able to provide an operational solution to solve for client onboarding by speeding processes and lowering risk;

Risk culture

Monitoring behaviour and organisational culture through unstructured data analytics using business intelligence combined with voice-to-text capabilities to improve communications surveillance, recognize behavioural patterns from data and, for example, to make rapid consumer suitability determinations.

Axis has helped many clients consider appropriate RegTech solutions with the right approach and supporting argument for a compelling business case to tackle their business challenges and also, deliver the right outcomes for the bank and their customers.

If you are facing similar challenges or perhaps you’re are losing patience with the traditional approaches from the market, we´d be delighted to help stimulate some ideas and support your business priorities to achieve the right business and customer outcomes.

Axis works with financial institutions to help to deliver their regulation, transformation and innovation agenda. We have helped clients to prepare for regulatory compliance, transform their business and operating models, and deliver new products and services to market. This often starts with just a meeting and a simple conversation with you.