In an era of mass personalisation and technological innovation, organisations increasingly need to make consideration of the way they use consumer data a part of their organisational culture. Since the GDPR's inception back in May 2018, there have been some encouraging findings (as I have discussed before) indicating that consumers are increasingly willing to share their data in exchange for personalised services and improved experiences. In addition, marketers are more confident about their reputation in the eyes of consumers. However, there is still a long way to go to improve consumer trust in marketing and highlight how data can be used as a force for good. Recent Adobe research reveals that over 75 per cent of UK consumers are concerned about how companies use their data.

In an era of mass personalisation and technological innovation, organisations increasingly need to make consideration of the way they use consumer data a part of their organisational culture. Since the GDPR's inception back in May 2018, there have been some encouraging findings (as I have discussed before) indicating that consumers are increasingly willing to share their data in exchange for personalised services and improved experiences. In addition, marketers are more confident about their reputation in the eyes of consumers. However, there is still a long way to go to improve consumer trust in marketing and highlight how data can be used as a force for good. Recent Adobe research reveals that over 75 per cent of UK consumers are concerned about how companies use their data.

One of the points I've been stressing for a long time now is that: It's not about data It's about business, the business outcomes, about the value that is generated for business. Business is the driver, data and what it produces is the enabler. As any other corporate asset, data's purpose is to generate business value. Organizations have apprehended the importance of data in their businesses and are looking deeper into data to gain a competitive advantage, implementing machine learning and artificial intelligence to achieve new business objectives and to move ahead of competitors in the industry. A data asset is every piece of data that organizations use to generate revenues, they are currently among its the most valuable assets, and organizations must invest seriously on managing these assets.

A quick glance over our technological, scientific, and productive history over the past few decades shows a trend towards increasing specialisation. Getting into an area and becoming a true expert in it takes considerably more time than it did several decades or centuries ago. Business, while progressing slower towards the same trend, is still experiencing something similar. Explaining in-depth technical concepts with sufficient detail and nuance to a layman is becoming more troublesome. Machine learning is one such example – frequently used, but scarcely understood by people outside the technical world.

Gaurav Kapoor is co-founder and President, MetricStream, Solutions and Services. The future will be digital; the past several pandemic years have shown that the shift to digital is happening even faster than expected. A McKinsey global survey found that companies' digital offerings and adoption accelerated by six to 10 years as a result of the crisis. This has led the way for the next generation to take advantage of digital tools as a way to invest, grow and experiment with new currencies. With the rise of digital assets like cryptocurrencies, NFTs and digital real estate in the metaverse, extreme disruption in this space is giving way to a new paradigm of risk.

Suddenly fruitful after years of sparse adoption, the long-awaited flowering of artificial intelligence (AI) and machine learning is upon us. Risk management and compliance leaders can expect these advanced analytic technologies will propel productivity-enhancing applications for years to come. But how did we get to this point? And as we enter the third decade of the 21st century, what can we anticipate right around the corner? AI owes its recent gains largely to the accumulation of big data assets and the continually declining cost of computing.

If you thought taking a few machine learning courses on Udemy might be enough to inure you against future unemployment then yesterday's report on machine learning in financial services from the Bank of England and Financial Conduct Authority (FCA) will come as a bit of a shock. The report is based on a survey of 106 banks and finance firms in London. It turns out that, yes, machine learning is being used in banks. But, no, it's not hard to find anyone to fill the roles and that this is the least of the worries as machine learning is rolled out across the finance sector. The charts below, from the report, show where machine learning (ML) is already most in use in the banking sector (defined as building societies, international banks, retail banks, UK deposit takers, and wholesale banks) and in the investments and capital markets sector (defined as alternatives, corporate finance firms, fund managers, principal trading firms, wealth managers and stockbrokers, and wholesale brokers.)

This is the final blog in a four-part series detailing the components necessary for AI success. You can read my earlier posts about cultural willingness, data and infrastructure readiness, and workforce skilling, before or after considering these four steps toward AI ethics, risk and compliance. Successful AI adoption requires forethought and preparation. Although AI itself requires a culture open to experimentation and learning from mistakes, when it comes to ethics, risk, and compliance, you can't simply wing it. Allocating the resources and planning for ethics is often a second thought, met by a certain amount of resistance in many organizations.

Regulatory and compliance issues are some of the most important, complex and resource-consuming problems to solve for any organization, especially for startups with limited resources. Over decades of development, regulatory requirements and documentation have grown into a matter of special expertise and skills to decode. Globally, $80 billion is spent on governance, risk and compliance, and the market is only expected to grow, reaching $120 billion in the next five years . According to ANZ, National Australia Bank has estimated that the cost of regulatory compliance has risen from $A86 million annually in 2012 to $A177 million in 2013 and $A265 million in 2014. Westpac was reported to spending $A300 million on compliance last year.

The Sarbanes-Oxley Act of 2002 (SOX) changed the way the U.S. looked at managing risk and compliance in the investment banking technology space. It is hard to believe that we soon will see the 15th anniversary of SOX. The Dodd-Frank Wall Street Reform and Consumer Protection Act, meanwhile, is in its sixth year of existence. While many changes have occurred in the technology landscape since 2002, one element has been constant: risk and compliance remain at the core of the banking industry as institutions continue to grow and deliver new, complex service offerings in this highly regulated environment. "With the emergence of artificially intelligent technologies, we must be mindful that these are tools and strategies that can be used to manage risk" As the scope of compliance continues to increase, the technology used to support this expansion also has an opportunity to grow.