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In the wake of advancement in technology and digital transformation, the financial services industry is making leaps and bounds .Risks such as fraud, theft and money laundering, are also escalating at the same pace if not faster.
As this had already been anticipated, Regulations for the financial services sector are already in place for institutions to adhere to and the IFLR1000 gives a splendid summary of the key financial laws in Kenya. Implementing and enforcing these regulations is where the rubber really meets the road.
Staying ahead in the game would require one to enforce these regulations through adopting technology. Here are some key elements to look at before narrowing down on “the one”
Really know your Customer
Know Your Customer (KYC) is one of the basic checks of any Anti- Money – Laundering ML system that would verify the identity of the client.This information is then screened against watch list databases of known fraudulent individuals and politically exposed persons.
We can go a step further and perform due diligence on beneficiaries associated to the said client in order to assess the risk and to deal with it prudently.
In some cases where the core system did not capture such details, one would require an AML system that enriches the existing data by allowing the institution to input the missing data.
The ideal system should also support White lists that enable the institution to reduce false positives. One can add customers who have matched with lists but are not deemed suspicious i.e. support for exclusions of names to avoid multiple false hits.
In built Analysis
Considering the amount of data that companies in the financial services industry collect, it would be impossible to analyze data manually. Tracking financial transactions as they traverse various jurisdictions in various channels would be the ideal way to go.
Highlighting patterns and establishing trends in the blink of an eye and taking the necessary action would help save you a not only time and money but from sleepless nights too.
The success of the solution you choose is dependent on its ability to integrate seamlessly into your existing environment.
Typical integrations would need to integrate into the Core system, CRM, Financial system, debt collection, IPRS. Ideally, these should not be point to point integrations as it would become a bottleneck altogether but rather enabling systems to share services read service oriented architecture.
The aim of having a keen eye on transactions is only to quickly spot suspicious activity. Having the ability to sift through tons of data and easily establish trends or patterns that lead to fraud and having measures in place to address this.
This could further give one a detailed report of suspicious transactions and the action taken to stop them.
At the heart of this is an engine that combines all the data from the various data sources and detect discrepancies at different levels of transaction processing. This may also have admin interface that allows creation of new rules, creation of customized rules read you can implement your own polices and the actions associated with each. These would range from creating triggers alerts of suspicious activity, stopping the suspicious transaction, and referring the suspicious transactions to a workload for review and comments.
The regulators definitely expect periodic reporting of all financial institutions to ascertain that they are fully compliant and adhere to good corporate governance. Retrieving this data is not a walk in the park for most. It’s actually more of mining data and anyone in the mining business can tell you the time and investment they have put into it to get it right. When it comes to this, leave it to the experts and give us a call.