A recent research conducted by BAE Systems revealed that 74% of business customers think banks use machine learning and artificial intelligence to detect activities related to money laundering. In reality, banks rely on human investigators to manually sift through alerts — a hard-to-believe fact selected only by 31% of respondents. This lack of automation and modern processes is having a major impact on the efficiency and expense when it comes to the fight against money laundering.

In recent years, three factors have heightened the risk banks face when combating financial crimes. First, the growth in volume of cross-border transactions and greater integration of the world’s economies have made banks inherently more vulnerable. Second, regulators are continually revising rules as their focus expands from organized crime to terrorism. Finally, governments have expanded their use of economic sanctions, targeting individual countries and even specific entities as part of their foreign policies.

Banks have responded to these trends by investing heavily in people, manual controls (“checkers checking the checkers”), and systems addressing point-in-time needs. For example, in the United States, anti–money laundering (AML) compliance staff have increased up to tenfold at major banks over the past five years or so. Banks have typically used a piecemeal approach, adding staff to areas with the weakest controls. Often this has resulted in compliance programs built for individual countries, product lines, and customer segments — with all the duplication that suggests. Banks have also hired thousands of investigators to manually review high-risk transactions and accounts identified through inefficient, exception-based rules.

Traditional improvements in operations, governance, and management of information systems will continue being important elements in financial crime prevention programs. But technology and advanced analytics can raise these programs to much higher levels of effectiveness and efficiency.

Today’s financial institutions are grappling with high volumes of customers, transactional and other internal data sets. If intelligently analyzed, these vast stores of data can prove the invaluable to institutions by helping them uncover financial crimes risk.

Across the financial services industry, there are substantial efforts to identify and exclude criminals who launder money garnered through various unlawful activities including illegal drug sales, human trafficking, fraud, and even terrorism. The laundered or “clean” funds are then used to refuel the world’s crime economy, supporting the lavish lifestyles and future crimes of those responsible for destroying the lives of millions.

Many financial institutions find themselves deficient in the domain of information technology and data analytic skills which are necessary to keep up with consistently evolving mandates and expectations, let alone the increasingly sophisticated criminals they are designed to curtail. Financial institutions have long used rules-based, legacy transaction monitoring systems (TMS) to detect and report transactions that are indicative of money laundering activity.

Money laundering is known to fund and enable slavery, drug trafficking, terrorism, corruption and organised crime. Three quarters (75%) of business customers surveyed see banks as central actors in the fight against money laundering. The penalty for failing to stop money laundering can be high for banks — and is not restricted to significant fines. When questioned, 26% of survey respondents said they would move their business’ banking away from a bank that had been found guilty and fined for serious and sustained money laundering that it had not identified.

The data contained in this release comes from 300 IT decision makers in the UK and the US, from organizations with 1000 employees or more, in a variety of commercial sectors. Interviews were conducted in February 2018, and were undertaken online using a rigorous multi-level screening process to ensure that only suitable candidates were given the opportunity to participate.

The research was conducted independently by Vanson Bourne, an independent specialist in market research for the technology sector.

To deliver the benefit of AI MicroMoney, a global provider of fintech services to the unbanked in emerging countries, partnered with Dbrain a decentralized platform for training artificial intelligence recently.

Under the partnership, MicroMoney will be able to leverage Dbrain’s AI platform to improve its scoring system that determines the creditworthiness of its customers.

Dbrain will deploy AI algorithms to evaluate customers’ data and make an initial assessment. It will help predict creditworthiness of each client, reduce the risk of loan non-repayment and prevent any attempts to cheat the system.

Dbrain uses Ethereum blockchain and smart contracts in conjunction with crowdworkers to create an ecosystem to build AI apps and business analytics. Dbrain’s Subjective Proof of Work (SPOCK) protocol uses crowdworkers from emerging markets to validate and label raw data. Data scientists are able to use this labeled data to train neural networks and create AI applications.

Dbrain also has developed Protocol for Indirect Controlled Access to Repository Data (PICARD) to facilitate the training process. The AI applications are able to provide businesses with new models and criteria for analysis and evaluation. Data providers, crowdworkers, data scientists, application developers and businesses can interact with the Dbrain ecosystem using Dbraincoin.