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Multiple wins for Pinsent Masons at China Business Law Awards

International law firm Pinsent Masons has won in three categories and two Belt and Road Deal of the Year awards at China Business Law Journal’s annual awards.

The categories the firm won for were:

  • Construction & Infrastructure
  • Education
  • Industrials & Manufacturing

Helena Chen, Joint Head of China said, “We are extremely proud to have received this recognition – which demonstrates the strength of our legal expertise in the Chinese market. We’ve been offering a complete range of legal advisory services to clients for more than 37 years, supporting businesses in both international and domestic operations. These awards reflect this, as well as our market leading position.”

Wei Liu, Head of China Corporate team said, “We have many long-term clients in these sectors, showing the trust they have in us and the services we provide. We are very delighted to be recognized in such a wide breadth of specialities.”

China Business Law Journal’s annual China Business Law Awards are judged on a number of criteria, including nominations received from in-house counsel and other qualified observers.

Pinsent Masons has also received two “Deal of the Year – Belt and Road” awards by China Business Law Journal for its work on the Greece MINOS 50 megawatts concentrated solar power project, and a Nigerian railway and port project.

Mark Hu, partner of Beijing office and the lead partner of the two winning projects, said, “We are thrilled to win the Belt and Road Deal of the Year awards again this year. We have been supporting Chinese clients on their outbound investments in Belt and Road countries since the inception of the initiative. We provide a chain of services throughout the life cycle of projects, including the investment, contracts, project development and financing, disputes resolution, compliance, and our experience spans across infrastructure and energy sectors.”

Pinsent Masons has been operating in China since 1983, and has three offices in Beijing, Shanghai and Hong Kong. Recent highlights in China include advising China Education Group on its AUD $128 million acquisition of King’s Own Institute, which was China Education’s first overseas acquisition, and one of the few overseas acquisitions by Chinese investors in the education sector. The team has also advised BP Ventures on its investment in R&B Tech, which was BP’s first investment in artificial intelligence (AI) technology in China.

How developers are helping traditional banks modernise

Bitcoin and blockchain tend to grab headlines in the world of banking. Cryptocurrency is the poster child of “disruptive technology” in the traditionally slow-moving finance industry. But, there are other areas where developers and software engineers must update business-as-usual in banking in order to survive.

According to one survey, 80% of bankers agreed that their institution “needs to complete an assessment over the next three years, but only 15% expected that to lead to a modernisation effort.” Security threats, the demand for mobile banking, and outdated core banking systems are all driving banks to consider massive overhauls to their IT systems. These are the biggest modernisation challenges facing financial institutions – areas where developers and remote software teams can play a significant role in keeping banks competitive.

Making updates to “legacy structures”

In the same survey, 60% of bankers reported that at least one of their major technology challenges is directly tied to aging core systems. “Maintaining legacy systems accounts for 78% of a bank’s IT budget, and 70% of bankers feel their core processes cannot quickly adapt to change,” reports Ripple.

Over time, banks have resisted major changes to their core banking system, the backend system responsible for processing transactions, updates to accounts, and maintaining other financial records. Core banking systems are in charge of processing deposits, loans, and posting credits, as well as updating other reporting and ledger tools.

As consumer-driven capabilities like mobile deposit and peer-to-peer transfer have grown, these core banking systems have had ad hoc updates – but no complete transformation. Core deposit systems were built in the 1970s, written in “old, inflexible programming languages” like Cobalt and PL/I. Oracle’s analysis also found that these “decades-old legacy core systems are inflexible, and each time a bank wants to launch a new product, they must ‘hard-code’ the system, which can take 12 months or more.”

There’s no simple solution to updating a bank’s core system: it’s a massive technological undertaking, but one that banks must invest in to serve its customers well. Engineers can help banks develop an agile, consumer-centric approach to core banking. There are multiple approaches to solving the problem of archaic core systems, and software teams can phase in iterative changes that evolve a bank’s core infrastructure without too much service interruption.

Modernising Fraud Protection

Fraud prevention remains one of the most difficult technological challenges facing banks as cybercriminals get more sophisticated in targeting consumers. To illustrate the challenge banks face in keeping consumer account information safe, Kasperky Lab hacked a “large, publicly-traded financial company in less than 15 minutes.”

The traditional approaches many banks have taken do not work. Authentication requirements and verification processes fail to prevent fraud and provide a negative customer experience. Instead, writes one security expert, “banks should focus on creating better systems and techniques to collect and analyse internal and external data, develop more meaningful algorithms and profiles, execute penetration testing against current strategies, detect changes in transaction patterns and develop more effective solutions.”

To protect consumers from malware and fraud attacks, banks must shift from a reactive to a preventative operations approach. Developers can help banks prepare by modernising the systems that store user data, moving information onto an encrypted cloud. IBM’s AI tool, for example, is said to offer a faster analysis of advanced persistent threats and attacks. Developers must integrate the latest technology into banks’ security systems to modernise.

Digital account opening

Developers will play a critical role in helping traditional banks keep up with the demands of customers on-the-go. Digital account opening is one process where developers and software engineers can have an immediate impact.

Digital account opening (DAO) is the process of opening a bank account without ever stepping foot inside a bank. DAO involves taking the following steps:

  • Collect a customer’s personal identification information
  • Evaluate and approve (or reject) a customer from a risk/fraud perspective
  • Verify the customer’s identity
  • Accept funds digitally and immediately, either through a debit/credit card or with mobile deposit
  • Sync with the core banking system

Many banks are capable of letting customers open an account online through a web browser. Yet, mobile-optimised account opening is an area where the industry has lagged behind. There are some very good reasons why this process is so difficult. Application fraud and strict anti-money laundering laws make it difficult for banks to meet regulatory requirements. An, there are significant security risks: in 2018, banks faced a more than $31 billion in global fraud loss.

But developers who help banks modernise to provide DAO will have an immediate financial impact. One report found that 69% of those surveyed wish to perform all their banking through online and mobile channels. BAI found that around 75% of millennials and more than 65% of Gen Xers prefer to use a digital channel to open a deposit account. The core consumer of the future will expect to be able to open an account, take out personal loans, and transfer funds from any device at any moment. Developers must find a way to build the infrastructure to allow banks to offer DAO.

This article was originally published at https://www.indexcode.io/

Battle of accountants versus machines

It is hard to go a day without seeing an article or a viewpoint in the media declaring Artificial Intelligence (AI) will make certain jobs redundant – but will it be the same for those roles carried out in the finance industry?

Technological advancements are at a faster pace than ever, with computers becoming more reactive and human-like in their responses and decision-making.

In January, a round on the American television quiz show Jeopardy was won by a computer named Watson, beating previous quiz show champions.

The rapid pace of technology advancement will no doubt see computers performing some accounting and finance functions, and this is already happening.

Recent innovations like mobile phone apps that can identify expenses from photos of source documents, and automatically allocate them to the accounting records, are already widely used across a range of industries.

In fact, last year HMRC confirmed it will begin rolling out AI to review tax returns and issue tax penalties.

Deciding to implement such technology in business must be well planned and researched. It is important that management make the decision in the context of their particular business.

For example, do they have the resources to employ this technology – certain pieces of software can be expensive and involve significant upfront costs before yielding any benefits.

Do staff have an appetite to adopt this technology? To maximise effectiveness, it is important that staff are trained and competent in using the technology on a regular basis.

How secure is the software and the devices used?

This is particularly important in the current world of big data, with the real risk of data breaches in large and small businesses across the globe, not to mention compliance with data protection legislation.

Whilst it appears inevitable that technology is developing to take over the more repetitive or basic accounting and finance functions, there are some positive aspects for use of this technology by accountants and businesses.

Not only will technology bring about new types of jobs that will be less repetitive and more interpretive (increasing employee job satisfaction), it will also free up management’s time to focus on value-adding activities.

Activities that can add to revenue (such as focusing on new markets, products and clients) or reduce costs within a business.

If firms are looking to the future but aren’t open to change, they will lose competitive advantage.

As Northern Ireland businesses increasingly compete on a global scale, the adoption of robotics and technologies is essential.

Rather than seeing technology as a threat, accountants and businesses should see it as a growing opportunity.

UK lawtech not yet disruptive, new research shows

Lawtech in the UK has a long way to go if it is to reach its potential, the Law Society of England and Wales said as it launched new research into the development and adoption of sector-specific technology.

In its Lawtech Adoption Report, the Law Society explores the UK’s burgeoning lawtech sector and highlights key developments in this area and what this means for the legal profession and the business of law.

Law Society president Christina Blacklaws said: “A range of drivers is accelerating development and adoption of lawtech, from an escalating need for efficiency, increasing workloads and complexity of work to client pressure on costs and shorter turnaround times.

“Some of the most notable growth areas are legal analytics, legal project management, governance and compliance and contract management.

“Lawtech in the UK is largely focused on efficiencies and automation rather than on delivering ‘new types of law’. As such it is less mature than other fields of digital disruption – such as fintech, where there is more funding and regulatory alignment.”

The business-to-business legal services market is the most mature, particularly within large law firms, where AI and machine learning-driven applications are ubiquitous. Some of the more established areas include collaboration tools, document management, IP management and e-billing.

The business-to-consumer legal market seems to be lagging behind. There is most traction in those law firms that are delivering large-scale commoditised services, where automation is principally all about driving efficiencies. For instance, chatbots, DIY law, robo-lawyers and triage tools are all becoming more common with a greater focus on the consumer experience.

“Our research found that law firms face barriers to adoption of many lawtech solutions that are fundamental to the industry, such as risks around compliance, the partnership and billable hours models,” Christina Blacklaws said.

“After several years of start-up activity, the sector is now ripe for a wave of consolidation and later stage funding. Adopting and pioneering new technologies will give firms a strong competitive advantage in a rapidly evolving legal services market.”

How AI and NLP will affect the media industry

One of the most visible of these technologies has been the use of artificial intelligence (AI) as well as Natural Language Processing (NLP), in process of collecting data and analysing it.

Publishing industry across the world is going through challenges. The dynamic nature of technology trends demands its continuous evolution from publishing to a digital media company. Progress has been made in terms of both content platforms i.e. the move from purely print to a variety of audio-visual avenues (such as television and online news portals, among others), as well as in terms of technology used to gather and publish information. One of the most visible of these technologies has been the use of artificial intelligence (AI) as well as Natural Language Processing (NLP), in process of collecting data and analysing it.

NLP is an area of AI which aims to produce effective human-computer dialogue by teaching computers to understand, organise, analyse and reproduce coherent sentences. NLP software uses a variety of models to achieve this – such as analysing large amounts of data to ‘learn’ a language and its rules as well as relying on manual input of the same rules. Currently, NLP algorithms enable large amounts of data to be absorbed and processed in order to give users the latest stories before they are discovered manually. Mobile news apps like Inshorts, NDTV and others use this process to display snippets in the notification bars, allowing users to be informed of trending stories with minimal effort. The ultimate aim is to allow computers to not only create sentences but to understand the direct and indirect meaning of those generated sentences. This would mean that the articles would contain perspectives too, rather than just facts, showing that the computer was smart enough to understand the language.

In the media industry, the concept of language can be extended not only to written words but to images and videos, as content types have become more diverse over the years. AI and NLP, hence, now possess a larger pool of data to work with and automate so as to generate news reports and other forms of required content.

They can do so in a variety of ways:

Data collection & Analysis

For a media company, there are two facets of data collection. Content in media industry is generated in all possible media formats (viz. text/image/video and audio). Text mining and NLP plays an important role in gathering semantic information about text, audio and video content. Relevant keywords, sentiments, and entities, along with topic classification is computed through NLP. Image classification is used for face detection or finding important parts of an image. All this data is then used to build a knowledge graph for ease of content search, recommendations, and relevant content syndication. The second part of data collection is around collecting time series data on what content is consumed by the end user. This helps the algorithm match user interest with content meta data to serve relevant content.

Real time serving data of web and mobile content helps the publisher to figure out trending content, which is attractive from advertising perspective. Additionally, it enables investigative reporters to obtain all known mentions in and around a topic they are working on within a span of minutes. The data obtained from text mining also serves as a learning process for the computer in order to develop its language skills for future endeavours. For a media company this requires significant investment in developing the infrastructure to collect and analyse large volume of data.

Insights Utilisation

The insights obtained from text mining allow for trends to be identified and leveraged in order to solidify a target audience and appeal to their interests. While this is applicable to new content, feedback from already published content allows for its improvement by using adaptable keywords. Historical analysis of trending content helps the editors focus on the topics they would want to write and distribute across social media. This means that when journalists are assigned specific beats, the algorithms showcase all the stories that relate to the beat immediately based on past data. Social listening tools imbued with NLP algorithms are the preferred mode of gathering the data necessary to achieve this as they can also gather insights into advertising along with long-form content. By serving relevant ads for a target audience, the ROI for the advertisers can be improved resulting in increased revenue for the publisher. High performing advertising campaigns, for example, then become templates for future campaigns and low-performing ones can be analysed for their defects.

Customer Feedback

Along with improving organisational content, NLP algorithms can also help formulate responses to questions posed by both customers as well as journalists. This would enable the system to also track the effects of the response across media platforms to determine the industry scenario. A direct outcome of this report would be the ability to weed out incorrect or ‘fake’ news that may be published by backlinking their sources and exposing their inaccuracies. Websites that publish spoofs and parodies, for example, can be identified and the information they disseminate fact-checked before it goes viral. Smart AI programs would be able to perform these semiotic analyses to determine accuracy.

The media industry today is becoming more dynamic and embracing technology at a faster rate to ensure that its progress can be harnessed. AI and NLP enable computers to understand semantics and determine the best course of action to be taken with respect to content dissemination based on the analysis of industry feedback.