In January 2018, scientists from Valladolid, Spain brought a piece of inspiring news to anticorruption advocates: they created an artificial intelligence (AI) system that can predict in which Spanish provinces are at higher risk for corruption, and also identifies the variables that are associated with greater corruption (including the real estate tax, inflated housing prices, the opening of bank branches, and the establishment of new companies, among others). This is hardly the first example of computer technology being used in the fight against corruption. Governments, international organizations, and civil society organizations have already been mining “big data” (see, for example, here and here) and using mobile apps to encourage reporting (see, for example, here and here). What makes the recent Spanish innovation notable is its use of AI.
AI is a cluster of technologies that are distinct in their ability to “learn,” rather than relying solely on the instructions specified in advance by human programmers. AI systems come in several types, including “machine learning” (in which a computer analyzes large quantities of data to identify patterns, which in turn enables the machine to perform tasks and make predictions when confronted with new information) and more advanced “deep learning” systems that can find patterns in unstructured data – in hundreds of thousands of dimensions – and can obtain something resembling human cognitive capabilities, though capable of making predictions beyond normal human capacity.
AI is a potentially transformative technology in many fields, including anticorruption. Consider three examples of the anticorruption potential of AI systems:
- First, corporations could use AI to design more effective internal compliance programs. Although there is widespread agreement that effective compliance programs should be “risk-based,” it is very hard for corporate compliance officers and other decision-makers to make nuanced, accurate decisions regarding corruption risk levels for different activities. An AI system could help, through analysis of both the relevant laws and regulations (through natural language processing) and past cases of compliance or non-compliance. An AI system could “understand” the content of regulations and learn to recognize patterns associated with compliance and non-compliance, and in so doing help identify risk areas in a way that allows the corporation to build an individualized compliance program. An additional benefit is that any subsequent revisions to the laws and regulations (or other relevant changes) could be directly incorporated by the AI system without any human intervention.
- Second, following the lead of the Spanish researchers, governments can use AI systems to identify vulnerabilities (both geographically and by sector). This would help governments target their efforts, focusing on stricter controls in those particular risky areas. Perhaps even more importantly, AI systems can help governments spot loopholes within the national or regional regulatory framework. (Without going into too much technical detail, when one variable identified by the AI system fails to respond as expected to changes of another variable, the AI system can send out an alert.)
- Third, AI can be especially helpful in the anti-money laundering (AML) context, helping investigators by increasing (potentially by orders of magnitude) the efficiency and accuracy of detection and due diligence. Ravn, a machine-learning AI platform based in London, has already demonstrated the power of AI systems, assisting the seven human investigators of the Serious Fraud Office (SFO) sift through and index 30 million documents—processing and summarizing up to 600,000 documents per day—in the Rolls-Royce corruption case. AI systems can also reduce “false positives” in banks’ current transaction monitoring systems (TMS). Currently, somewhere between 90% to 95% of all alerts are false positives, according to a PwC industry survey; this is because the traditional TMS is built on unsophisticated model extracting data from fairly broad/crude human-identified risk factors. Reviewing all these alerts requires the involvement of thousands of personnel, the annual cost of which reaching hundreds of millions of dollars. Hence any decrease of false positives would enhance significantly AML’s efficiency and help investigators focus on the right cases.
Of course, AI is no cure-all. Although AI reduces the need for human personnel to perform routine tasks, humans are important to steer AI in the right direction and to funnel the “supervised” information into the AI system when it is first developed. And AI systems also raise transparency concerns, as many stakeholders (and sometimes even those who designed the systems) cannot make sense of AI at the algorithmic level. Some are understandably uncomfortable entrusting important financial or personal information to a “black box,” and such concerns should be taken into consideration when designing or promoting AI technology in politically and socially sensitive areas such as anticorruption. Nevertheless, a hybrid of human efforts and the transformative power of AI technology has the potential to enable compliance officers, governments, investigators, and others to unearth the truth hiding amid thickets of data, and in so doing empower both public and private sector actors to fight corruption more effectively.