In the ABA/ABA FinCEN (Financial Crime Enforcement Network) conference 2019, the acting director Ken Blanco discussed the introduction of new divisions for transforming the current AML/CFT regimes in the UK. There were entire new divisions of enforcement and compliance along with global investigations to restrict financial crimes. Until now the current Anti-Money Laundering (AML) landscape was struggling to gain some form of momentum. However, with the implementation of the recent Anti-Money Laundering Act 2020 and AML solutions with integration of AI and ML, hopefully, 2022 will be remembered as a year proven to be a turning point for financial institutes. It is surprising to note that in the year 2020, banks from all over the world paid a total of $15.13 billion dollars and the US held the first rank in those AML fines, a sum of $11.11 billion was paid.

Editor's Note: This deep dive companion to our high-level FAQ piece is a 30-minute read so get comfortable! You'll learn the backstory and nuances of BERT's evolution, how the algorithm works to improve human language understanding for machines and what it means for SEO and the work we do every day. If you have been keeping an eye on Twitter SEO over the past week you'll have likely noticed an uptick in the number of gifs and images featuring the character Bert (and sometimes Ernie) from Sesame Street. This is because, last week Google announced an imminent algorithmic update would be rolling out, impacting 10% of queries in search results, and also affect featured snippet results in countries where they were present; which is not trivial. The update is named Google BERT (Hence the Sesame Street connection – and the gifs). Google describes BERT as the largest change to its search system since the company introduced RankBrain, almost five years ago, and probably one of the largest changes in search ever. The news of BERT's arrival and its impending impact has caused a stir in the SEO community, along with some confusion as to what BERT does, and what it means for the industry overall. With this in mind, let's take a look at what BERT is, BERT's background, the need for BERT and the challenges it aims to resolve, the current situation (i.e. The BERT backstory How search engines learn language Problems with language learning methods How BERT improves search engine language understanding What does BERT mean for SEO? BERT is a technologically ground-breaking natural language processing model/framework which has taken the machine learning world by storm since its release as an academic research paper. The research paper is entitled BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (Devlin et al, 2018). Following paper publication Google AI Research team announced BERT as an open source contribution. A year later, Google announced a Google BERT algorithmic update rolling out in production search. Google linked the BERT algorithmic update to the BERT research paper, emphasizing BERT's importance for contextual language understanding in content and queries, and therefore intent, particularly for conversational search. BERT is described as a pre-trained deep learning natural language framework that has given state-of-the-art results on a wide variety of natural language processing tasks. Whilst in the research stages, and prior to being added to production search systems, BERT achieved state-of-the-art results on 11 different natural language processing tasks. These natural language processing tasks include, amongst others, sentiment analysis, named entity determination, textual entailment (aka next sentence prediction), semantic role labeling, text classification and coreference resolution. BERT also helps with the disambiguation of words with multiple meanings known as polysemous words, in context.

If the financial industry has taught us anything in the past, it is that we can no longer postpone digital transformation in banks. Today, consumers expect to perform banking transactions from anywhere and anytime. The technological advancements that offer improved interface and inclination of customers for convenience drive the market of online banking. According to a research firm, Allied Market Analytics, the global online banking market is expected to reach $29.98 billion by 2023, with a compound annual growth rate (CAGR) of 22.6% during the period 2017–2023. The increased demand for digital versions of the traditional bank has caused several financial institutions to seek out better solutions to securely digitalize their offering and transactions while maintaining low costs.

The need to provide an enhanced customer experience is the primary factor augmenting the growth of the AI in BFSI market. As the competition among the market players is mounting day-by-day, companies have started to focus on providing a better experience to the customers to gain the customer loyalty. This encourages financial institutes to integrate advanced analytics tools and solutions to analyze customer data to fulfil their requirement, understand the customer experience, and to make smarter predictions about their behavior and requirements. Furthermore, companies are also looking forward to connecting with the customers on their choice of channels to provide a more seamless experience. Furthermore, as digitalization is spreading across the globe, customers are becoming more empowered.