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Artificial Intelligence and Machine Learning system development for assisting in complying with AML, CFT, KYC, BSA, AMLD, FATF, CBRIC, SBP, SECP and other banking regulations.
Our proposal for AI for AML
AI/ML (Artificial Intelligence and Machine Learning) technologies can effectively improve, automate and optimize anti-money laundering (AML) transactions. These technologies can scale to handle the volume, velocity, and variety of data that is generated by the today’s financial institutions while being able to counter the ever-evolving approaches of bad actors to money laundering. For financial institutions, the time is now to deploy AI/ML into their ecosystems. AI/ML offers real solutions to reducing risk related to financial crimes, fraud, compliance, and AML.
As mentioned in the DFI document the objective is as follows :
We believe that the AI/ML technologies proposed shall assist in the reduction of the workload on the bankers who are tasked with AML related compliance.
This allows banks to more easily increase the number of transactions while reducing the errors encountered and also reduce risk of regulator fines.
Issues related to
AI/ML when it comes to AML compliance.
(2) the notion of AI/ML being a “black box” where the inner workings are not clearly understood by business people, regulators, and compliance officers; and
(3) The cautious tack taken by regulators requiring compliance officers to demonstrate understanding and provide validation about the results provided by the models.
We are not proposing a replacement of the bankers. We are proposing a complementary system where the AI/ML assists bankers.
Things which can be developed to assist bankers in reducing their overheads in compliance via AI/ML.
The generation of
the alerts themselves. Traditionally these alerts have been generated
based on a set of rules most of which are hand-coded and a few rely
on rudimentary data mining and statistical techniques.
Robotic Process Automation (RPA)
Customer Due Diligence (CDD).
Use cases within CDD
include customer setup, onboarding, refresh and enhanced due
diligence. During refresh, for example, RPA can be used to validate
existing customer information, pulling customer data from various
internal repositories to verify the customer’s information or to
hand off to an associate for review.
Customer Screening.
RPA can help compile
and consolidate customer information from multiple databases and hubs
and send to screening vendors or compare directly to watch lists. RPA
can also perform first level reviews and determine if screening
results are “actual cases” or “false positives” based on
predetermined business rules. As is the case with CDD, RPA can manage
screening based on the customer’s risk level.
Transaction Monitoring.
The most important
use case in transaction monitoring is “alert review”.
Offboarding.
Institutions
determining when to close accounts or when to place a customer on a
“Do Not Do Business” (DNDB) list can use RPA to check the
client’s account status and provide insights on account activity.
Estimates for the
system development :
Prototype costs : Rs
5,000,000
Product Release : Rs 60,000,000
Project Phases
As a rule, the Artificial Intelligence project cost depends on the work being done to develop a product. The development work is usually split into several phases. Having a general idea of the project phases may help you make a rough estimate of its cost. The following application roadmap is adopted by Azati when developing systems based on ML algorithms.1. Discovery & Analysis Phase
The purpose of this phase is to conduct a feasibility study and set business and project objectives.The work on a project starts with analyzing the customer’s business processes, data assets, and current metrics. At this stage, the project team defines success factors (expected metrics improvements), applicable technological stack, timeline and budget, and reflect them in the corresponding documentation.
The parties find out whether or not the AI concept is possible, and if it is, define the scope of work needed for the next move, namely prototype development.
If all critical data, processes, and metrics are available in the required format, the phase takes up to 2 to 3 weeks.
2. Prototype Implementation and Evaluation Phase
A prototype is a business model created to test feasibility and proof of concept. It can be a limited, text or drawing-based mock-up, or a more sophisticated code-based prototype. Its form depends on the project complexity and tools (screen generators, application simulation programs, or design tools) used to develop it. Prototypes are shown to and discussed with the client.Prototyping is a great technique that allows software professionals to validate requirements and design choices. Prototypes are quick and cheap to produce, and flexible to adjust. The risks and costs associated with software implementation are significantly reduced, as the requirements are well-discussed early on before development begins.
3. Minimum Viable Product (MVP)
An MVP is a real product with a set of functional features developed on the basis of the prototype findings. The MVP relies on the client’s actual data and is exposed to a small group of real customers as a simplified version of the ultimate product solution. The feedback is very relevant, as it is way less expensive to modify the system at this stage than when it is fully developed.4. Product Release
At the last stage, the product with a complete set of predefined features is developed and then launched into the market. The preceding steps put a lot of emphasis on the requirements elicitation and validation – therefore, the end product is made with minimal risks. The cost of this phase is usually estimated during the previous stages.Proposing company : Applied Technology Research Center.
Web : http://atrc.net.pkContact information : http://atrc.net.pk/contact/contact.html
Regards,
Khawar Nehal
khawar@atrc.net.pk
92 331 2036 422
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