There is an extensive increase in the amount of pressure put by the regulators on the financial institutions regarding the tackling of the financial crimes. Due to this, the costs of compliance is sky rocketing. The operating costs on the compliance have been raised by 60 per cent in comparison to pre 2008 financial crisis levels, according to the findings by Deloitte. The overwhelming need for the innovation is to reduce the burden of these cost compliance. Luckily, these innovations are helping the financial institutions with the cost compliances, their need and future directions too, that too from their data driven insights. The right technology can give banks the capacity that can lower false-positive alerts from transaction monitoring and screening, find more risks and mechanize investigations and KYC (know your customer) processes.
The current AML ( Anti money laundering ) systems are not that efficient in compliance programs. One of the prime issues faced by the financial institutions is Money laundering, that regulators are pressuring about. The United Nations Office on Drugs and Crime founded out an estimate of about 2 to 5 per cent of global GDP (gross domestic product) is laundered every year and the foundation of the solution lies in understanding the data. Few of the big B’s in the world are given the task of processing around 50 million transactions per day from their thousands of customers, who are spread across the globe. And this creates a hugely complex situation, one anxious with potential error and lack of visibility.
Existing AML-compliance lineups are often relying on people more than technology. Typically, 60 to 70 per cent of compliance budgets are allotted to the investigators revising the alerts and analysts completing KYC checks and periodic evaluations. By intelligently automating these processes, administrations can become more disciplined and balance the compliance costs across the institution.
What the traditional AML- monitoring tool does is that, it compares an activity to fixed pre-set thresholds or patterns to determine if it is unfamiliar. This creates a status quo which makes the bank becomes susceptible to the criminal activities, and the sophisticated criminals can easily invade the bank’s rudimentary controls and carry out their illegal activities without any one’s notice. These panels also produce high false-positive alerts that each require a human review from already strained teams to assess whether it truly stances a risk or not. An astounding 95 per cent of alerts turn out to be false positives, taxing the administration’s time and human-power resources and making it available for the compliance failures and potential finance or legal consequences.
The lack of context in the organization’s data is the root cause of these issues. Poor data quality and soiled data is equivalent to inviting compliance issues. Especially when these financial institutions are unable to connect internal and external data base. This is where AML investigators come into the depiction. Their role is to study each company and ask appropriate questions about what the business does, its industry codes for, its size, the number of subsidiaries, who owns it and so on. Conversely, this manual data alliance wastes more time and means agents are carrying out low-value work, adding to the costs of compliance.
To be actually operative, financial institutions must look to build an able, data-driven enterprise. By organizing these innovative technologies, such as entity resolution and network analytics, banks can ensure high data excellence and build a single background data view, assertively enabling the automation of decisions. And with this, financial institutions can create reasonable decision models and remain liable to the regulator while simultaneously reducing the risks and even the compliance costs.