AI and advanced analytics in AML: From rule-based controls to intelligence-led capabilities

<h2>Supporting AML with machine learning</h2>

<p>AI is a broad term covering multiple fields. For AML professionals, perhaps the most relevant subfield of AI is machine learning, which refers to the use of algorithms to continually improve a task, without the need for human intervention. Machine learning algorithms search for patterns within a given data set. Repeated recognition of patterns allows an algorithm to make ever more swift and accurate predictions.</p>

Making the Most of Machine Learning: How will AI affect business process outsourcing?

Watch our video above to learn how AI will affect business processing and oursourcing from:

-Stanton Jones, Director and Principle Analyst, ISG

Making the Most of Machine Learning: How will AI change organisational culture and structure?

Watch our video above to learn how AI will change organisational culture and structure from:

-Stanton Jones, Director and Principle Analyst, ISG
-Suhir Jha, Head of Product Management Strategy, Infosys 
-Cliff Justice, Principle, Innovation & Enterprise Solutions, KPMG

Making the Most of Machine Learning: What are the advantages for business?

Watch our video above to learn about the advantages Machine Learning can offer your business from:

-Cliff Justice, Principle, Innovation & Enterprise Solutions, KPMG
-Suhir Jha, Head of Product Management Strategy, Infosys 

Making the Most of Machine Learning

Research conducted by the Economist Intelligence Unit (EIU) and written in discussion with SAP shows that many organizations are moving ahead now, some aggressively, to integrate ML into their operations. For example, the survey of 360 organizations shows that on average 68% use ML to at least some extent today to enhance their business processes.

Making the Most of Machine Learning

ML is not just a technology; it is core to the business strategies that have led to the surging value of organizations that incorporate it into their operating models—think Amazon, Uber, and Airbnb. Fast Learner organizations get that. 

Fewer Fast Learners than other organizations suffer from a lack of strategic clarity about ML. And fewer are plagued by organizational resistance to change. The reason may be that ML is viewed as more than a tactical tool for simply automating away costs and people. 

위험과 보상: 머신러닝의 경제적영향에 관한 시나리오

인공지능(AI)과 그 주요 분야 중 하나인 머신러닝의 발전에는 특히 기술의 사회와 경제에 대한 영향에 관한 현재의 논쟁이 시사하는 것보다도 불확실성이 더 큽니다. 물론 진정 놀라운 발전이 있었으며 지지자들이 이를 강조하는 것도 맞습니다. 10년 전만 해도 자동차가 통제된 환경에서라 해도 자율주행할 수 있다고 믿거나, 알고리즘이 사진을 분류하고 정리하는 방법을 학습할 수 있다고 믿는 사람은 소수에 불과했습니다. 그러나 지금은 그 둘 다 가능할 뿐만 아니라 다양한 형태의 인공지능이 일주일이 멀다 하고 새로운 작업을 수행하고 있습니다.

Risks and rewards: Scenarios around the economic impact of machine learning

There is more uncertainty around advances in artificial intelligence (AI) and one of its major sub-sets, machine learning, than the current debate suggests, particularly with regard to the technology’s impact on society and the economy. No doubt the advances have indeed been incredible and advocates are right to highlight them. However, not everyone views this as an unalloyed good. In fact, there is great concern that AI poses a threat to jobs, privacy, and, eventually, even humanity.

Putting machine learning to work on your cyber-security front line

Cyber-security is a top concern for all businesses with the annual global cost of cyber-security predicted to reach US$6tn by 2021, up from US$3tn in 2015.

The future of financial services: Transforming an industry

Enjoy in-depth insights and expert analysis - subscribe to our Perspectives newsletter, delivered every week