“Compliance by Design”
Anthropology and Compliancehas stronger correlation and have led to create stronger Culture of Compliance across organizations. Most of the Human behavior are motivated by – need (we have to), by desire (we want to), or conformity (my fellow citizen , friend or competitors are doing it)-So as when we refer to the term to foster culture of compliance its often we the word culture which needs attention.
Regulatory Change management:
The Biggest concern for any organization should be when the most passionate becomes the most quiet–Hence The Change Management is very sensitive and critical aspect because it is to deal with emotions, beliefs , inherited culture , sometime values and practices of individuals in an organization and we see the core behavior patterns either adherence or the resistance to change is driven by various motivations behind the non-adhere or adhere. In academic anthropology, the term “culture” denotes learned behavior, both in words and action, passed on within a group. The “culture” of a tribe or group is always changing, and it is always in dispute;
Regulatory Compliance the Right things to do Vs Easy ways to do: (Author Abstract)
The Ethics /compliance emails from corporate compliance team will lead to zero amount of “team building” or rhetoric branding can make disgruntled employees happy if they are being treated harshly by their superiors. In the same way, no amount of talk about “doing the right thing” can replace the conditions necessary when people just do the right thing “naturally.” Most people who do the right thing have no formal “code of conduct.”They have not passed some kind of “ethics test.” If asked (and this is what anthropologists do), people might explain our naturalness in doing the right thing as “tradition” or “the culture” or “the way we do things around here.” We don’t even have to talk about it. We just do it. It’s “tradition.” Social scientists call it “practice,” or in more academic terms, our “habitus.”
Deficiencies in Regulatory Complianceleads to the Hugh penalties and reputational losses, no brainer end result being loosing trust of clients and impacting economy of the cross border financial institutions largely.
Top Regulatory fines / breaches in 2020 were :
- Commerzbank fined £37.8 million by FCA for AML failings
- Capital One fined $80 million for 2019 data breach
- OCC issues $85 million penalty to USAA Federal Savings Bank
- EB fined $107 million by regulator for Baltic AML failures
- Deutsche Bank faces $150 million fine for Jeffrey Epstein ties
- Western Union refunds $153 million for scam victims
- Citi to pay $400 million OCC fine for risk management failures
- JP Morgan charged $920 million for market manipulation
- Wells Fargo agrees to pay $3bn fine for fraudulent account furor
The lack of consistent and effective controls over organizational business processes can cause serious penalties and negative results for a company’s reputation and even jeopardize its existence. There is a need for continuous monitoring of controls and systematic collection and evaluation of relevant data. Compliance management is essential for ensuring that organizational business processes and supporting information system are in compliance with laws, regulations, and various legislative or technical documents pertaining to the place of business. It’s critical that smart technology needs smart design inherently for changing landscape of the Regulatory Compliance So this changing landscape of regulation across cross borders requires COMPLIANCE -By-DESIGN
Is a process of developing a software system that implements a business process in such a way that its ability to meet specific compliance requirements is ascertained ? Formal methods are typically involved to automate compliance rule verification.
Strategic Challenges of Compliance:
Cost imperative compliances implementations. Identifying the right skill and right technology solution to form eco system, which can support the right compliance output. How can the business build a flexible and agile Compliance operating model to meet its specific needs across the enterprise? These are challenges, which technologists and regulators face.
Compliance by Design-In regulatory reporting, key areas of AI use have been in managing and validating data, validating results against predetermined criteria, and monitoring overall compliance The Machine Learning: The advantages of such a data-driven modeling approach over traditional business rules are significant. Since they are written by people, business rules are only as good as the knowledge of those people. (Humans can write rules to detect fraud patterns that they already know about, whereas machines can infer fraud patterns 1 directly from the data itself—and not previously known to people.) Why machine learning is a potential game-changer for anti-bribery compliance the financial services industry—which, given its business model, is heavily exposed to fraud risk—has led the way among industries in leveraging machine learning technology. Its algorithms and data labels pointing to (or predicting the likelihood of) credit card fraud, money laundering and other crimes are relatively mature because of the volume of data processed over time. We believe that machine learning holds tremendous promise in addressing bribery and corruption risk as well— offering compliance and risk teams a significant boost by processing, identifying, and tiring potential anomalies (or “exceptions”) that may be hiding in their data. It can scour transactional data and communications for traditional corruption markers—duplicate payments, improper relationships, offshore bank accounts and the like—and prioritize accordingly. This technology can also be extremely effective in an M&A context—where an acquiring company needs to quickly process many potential exceptions from a target company or legacy technology. Machine learning can help your team quickly tier these exceptions for faster human action. The world is dynamic, and fraud patterns are constantly changing. Keeping the manually coded business rules up to date can become a difficult and costly exercise. Machines can be continuously trained and models 2 continuously updated with much less effort. Encoding human knowledge into a set of business rules is a challenging task in the first place. It would be much easier for domain experts to simply show machines examples of fraud and let them come up with the rules (or 3 models) automatically
(Abstract Author courtesy: (Culture and compliance: an anthropologist’s view) (https://portal.research.lu.se/portal/files/5565769/5204645.pdf)
Author – Manorama Kulkarni
Director -Capgemini I Financial Crime and Compliance I Asia-Europe