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AI and humans must work together to turn dreams into reality

In May 1997, a world record was set for the first machine to defeat a world champion when Garry Kasparov, Russian chess grandmaster and former world chess champion, lost to Deep Blue in a rematch held in New York City.

While contemplating his loss to artificial intelligence, Kasparov postulated that humans and machines could potentially form a perfect synergetic relationship. And his theory came to fruition when, in 2005, a bunch of amateur chess players controlling three computers beat a team of grandmasters.

Humans possess intuition, strategy and experience. Machines are obedient and great at calculations, tactics and memorizing. We set the goals and formulate hypotheses. We determine the criteria and machines will perform the routine work. We then evaluate the results and insights. The consequence of this symbiotic relationship between humans and machines is work that is far superior to what humans alone can perform.

Newsweek cover showing Garry Kasparov before he challenges IBM’s Deep Blue computer to a chess rematch (Image Source: pinterest.com)

Machines are good in computing. Machines are great in following instructions we have programmed into them. Humans, however, have purpose. We possess passion. We have dreams of a better world.

Deep Blue was victorious, but was it intelligent? No, no it wasn’t, at least not in the way Alan Turing and other founders of computer science had hoped. It turned out that chess could be crunched by brute force, once hardware got fast enough and algorithms got smart enough. Although by the definition of the output, grandmaster-level chess, Deep Blue was intelligent. But even at the incredible speed, 200 million positions per second, Deep Blue’s method provided little of the dreamed-of insight into the mysteries of human intelligence.

Kasparov versus Deep Blue, 1997 (Image source: rauserbegins.com)

Instead of worrying about AI taking over our current jobs and rendering us all out of work, we should be thinking about what AI and machines still cannot achieve. For we will need their help to turn our far-reaching dreams into reality.

Rather than dreading artificial intelligence, we should fear our own complacency and limited ambitions. We need to overcome our own easily-contented attitude and seek out man-machine solutions to ever more challenging problems currently plaguing humanity.

Machines have calculations. We have understanding. Machines have instructions. We have purpose. Machines have objectivity. We have passion. We should not worry about what our machines can do today. Instead, we should worry about what they still cannot do today, because we will need the help of the new, intelligent machines to turn our grandest dreams into reality. And if we fail, if we fail, it’s not because our machines are too intelligent, or not intelligent enough. If we fail, it’s because we grew complacent and limited our ambitions. Our humanity is not defined by any skill, like swinging a hammer or even playing chess. There’s one thing only a human can do. That’s dream. So let us dream big.

Artificial Intelligence cannot touch you if you are unpredictable

Jobs in highly-predictable environments can be automated easily!

Technologies such as artificial intelligence will automate 50% of the current activities that our workers carry out today. This is especially true if they work in highly-predictable environments like accommodation and food services, manufacturing, transportation and warehousing, agriculture, retail and mining.

Jobs that are least susceptible to automation would be work that requires expertise in decision making and planning, creative tasks, people management and development, stakeholder engagement, and performing physical activities and operating machinery in unpredictable surroundings.

The advent of AI and automation doesn’t mean people are not needed in the workforce anymore. On the contrary, they will create new jobs that most of us cannot even envision yet. Many workers will be upskilled to work alongside smart machines.

The broad usage of machine learning across industries

Machine learning is finding its way into practical applications across many industries.

  • Agriculture: crop personalization depending on weather and environmental conditions
  • Finance: Fraud identification, personalized financial products
  • Pharmaceuticals: personalized health outcome prediction, customized remedies
  • Media: Personalized advertising, new consumer trend discovery

Source: What’s now and next in analytics, AI and automation

 

Malaysia Bank Adopts Artificial Intelligence Early Warning System to Reduce Risks of Non-Performing Loans (NPLs)

The client is one of the largest international banks, headquartered in Singapore with a prominent presence in the Asian region. As a Financial Institution with significant focus on commercial loans, the challenge of managing credit risk from loans turning delinquent is pervasive and a major concern to the bank. With a clear understanding that high NPL ratios demand greater loan provisions, which reduces capital resources available for lending and dents the bank profitability, the Bank decided to embark in this journey to mitigate the risks of NPLs.

The Bank was using manual excel-based spreadsheets to forecast potential delinquencies based on historical data and repayment behaviors. This manual process was labor intensive, time consuming, lacking futuristic prescriptive analytics modeling and its precision was questionable. Scalability of analysis was a constant challenge with changing economic climate and trends.

Pounded by these challenges, the Bank realized its need for an automated and intelligent solution with the capability to provide early warning for delinquent accounts.

Solution

With a significant double-digit increase in the number of approved loans, the Bank prioritized its focus on effective management of loans recovery and delinquencies. The Bank realized the need for automation coupled with deployment of Artificial Intelligence (AI) instead of the labor intensive manual processes which lent a toil on the credit management teams.

The conventional CRISP-DM (Cross Industry Standard Process for Data Modelling) breaks the process of data mining into six major phases of which the sequence of the phases is not strict and moving back and forth between different phases is always required. This conventional model usually requires disparate tools and trained statisticians to navigate usage of these tools for data mining.

Juris Technologies has taken a distinctive forward-looking approach in bringing Machine Learning to the next level.  Leveraging design thinking and embedment of wizards, the business users are empowered with the power of machine learning and artificial intelligence to apply their own domain knowledge and hypothesis in training the different analytical models.  By empowering business users, organizations reap the benefits of significant improvements with the capabilities of continuous training of analytical models.

The Bank engaged Juris Technologies, an end-to-end broad spectrum customer and credit management solutions provider to automate workflow and processes for loans management. With precise understanding of the Bank’s need, Juris Technologies proposed and delivered the Juris Mindcraft solution, a system powered by Artificial Intelligence capabilities, to predict potential Non-Performing accounts as early as 6 months prior to delinquency.

Juris Technologies utilized the Bank’s readily available data, consolidated various attributes of Term Loan, Trade and Overdraft services of accounts, and applied Artificial Intelligence prescriptive models. These models combined historical accounts behavioral analysis with future predictive analytics and algorithms to identify and segregate accounts potential of deterioration to NPL status. In addition to analytics, the solution also provided the Bank’s users with insights, rationale and rules for the identification of such accounts, enabling strategic and tactical actions to be deployed in managing and reducing NPLs.

Benefits Obtained

Combining the capabilities of the Bank’s proprietary Credit Scoring Model with Juris Technologies’ early warning and account monitoring system, the Bank successfully enhanced its existing credit assessment structures and risk assessment capabilities. Coupled with improved economic climate, the Bank was able to reduce NPLs significantly from a sharp hike to a plateau.

With reduction in NPLs, the Bank enjoyed increased profitability and improved their ability to provide new lending and a healthy working capital.

Industry: Banking

Big Data Analytics Solutions ProviderJuris Technologies is a Malaysia-based, MSC Status company providing innovative business solutions in the financial services and telecommunications industry.

Juris Technologies started business operations in 2003 with a primary focus in building solutions serving the Banking, Financial Services and Insurance (BFSI) and Telecommunications industry. Our solution consists of an integrated framework of scalable components which is used to build enterprise quality solutions.

Our solution framework is used to design and develop solutions for customer relationship management with modules such as Contact Management, Campaign Management, and Sales Force Management. In the areas of Credit Management, we provide solutions for Customer Acquisition, Loans Origination, Credit Scoring, Conveyancing and Litigation, and Collection and Recovery. Today, we are the leading customer and credit management solutions provider in Malaysia, offering a collaboration platform for over 1,000 external parties including legal firms, collection agencies and valuers.

Our key clients are Affin Bank, Agro Bank, Alliance Bank, Bank Kerjasama Rakyat Malaysia (BKRM), Bank Persatuan, Bank Simpanan Nasional, CIMB Bank, CTOS, Hitachi Capital, HSBC, JCL, Maybank Etiqa, OCBC Bank, SME Bank, Standard Chartered Bank and Tenaga Nasional Berhad (TNB) Malaysia.

Our global footprint for the licensing of Juris Application Server spans across 22 countries and  the Juris Application Server framework is used by organizations such as Sony USA and The Software Engineering Institute of Carnegie Mellon.

Singapore Bank Embraces Machine Learning To Boost Their Advertising Campaigns

The client is one of the largest banking and financial services corporations in Asia. Headquartered in Singapore, the conglomerate has operations across multiple countries around the region.

The bank has a substantial budget for digital ad campaigns. In the past, however, a large portion of their digital advertisements were not optimized; their ads were reaching many viewers who either had no interest in their products or lacked the propensity to purchase anything from the bank.

In fact, if we look at the current digital ad landscape, 54% of digital advertising in general goes unseen, resulting in wasted impressions and opportunities. Marketing channels as well as audience are highly disjointed in this digital age. Audience fragmentation has made it nearly impossible for marketers to manually segment their target market and perform targeted selling. Also, if they were to miss out key segmentation variables or channels, opportunities to convert would be lost.

The bank realized that there was a need to optimize their ads – automatically and in a very intelligent manner.

Solution: The opportunity for the bank was to continuously find the best target audience for their ad campaigns. And this process had to be done automatically instead of manually tweaking the hundreds of parameters staring at the marketing team in the face.

The bank engaged Optimate, a campaign optimization engine that uses self-learning artificial intelligence (AI) to automate and enhance their digital advertising projects. Optimate has a delicately-designed AI system that uses data-driven logic, learning from historical campaigns and matching promoted ads and products with interest keywords in their proprietary knowledgebase.

Centered on the bank’s desired target market, as well as content from ad creatives, the system would dynamically search for the right audience. Optimate applied sampling-based methodology to augment the scale of data, and to generate the most appropriate target audience based on evidence provided by the data itself.

Optimate AI Overview

The ad optimizing engine was also able to diagnose the status of the client’s ad account via a Competitiveness Index (CI) from all the data within the account linked to their system. A/B testing automatically carried out on different ad creatives by the self-learning algorithms before being pushed to the target audience.

Further budget adjustment analysis was also provided to the bank to complete the system’s set of automatic ad campaign creation and optimization solutions.

Benefits & ROI: The bank’s overall ad effectiveness improved tremendously; the AI engine perpetually worked in the background to fine-tune and deliver the ads to the right audience, all in real-time.

In the two campaigns that were optimized by Optimate’s AI engine, the bank managed to achieve up to 36% increase in reach, 48% reduction in cost per link click, 41% reduction in cost per post engagement, and 50% increase in relevance score.

In a short span of 4 days, the bank managed to cut down their campaign cost by 20%.

And the good ROI will only get better as more campaigns are run for longer periods of time. The more valuable data is generated and analyzed by the AI engine, the more effective the ad optimizations will be.


Did you miss last week’s Optimate Webinar on the Future of Marketing 2017? You can view the recording here. The webinar covered common FB marketing mistakes, 2017 marketing trends, and how SMEs can tap into AI-driven, automated marketing tools for runaway marketing success.

As someone who runs digital ad campaigns, you stand to benefit from Optimate’s sophisticated AI marketing engine. You can sign up for one of their plans using the discount code OPTDECWEB.

Industry: Banking
Big Data Analytics Solutions Provider: Optimate is an AI optimization engine for multiple channels of marketing. Combining self-learning artificial intelligence (AI) to automate and optimize online marketing with a cloud platform, Optimate enhances businesses’ marketing activities by streamlining the process of serving the best content to suitable audiences using machine learning. Optimate now serves over 100 local and regional clients, ranging from Fortune 500 companies to local SMEs. www.optimate.co

Confess Now, Or The Taxman And Big Data Analytics Will Come After You

Governments worldwide are under mounting pressure to tighten their tax gaps, defined as the difference between what the government expects to collect in taxes versus what is actually collected. Closer to home, in a 2011 Economic Transformation Program (ETP) update by Minister in the PM’s Department and CEO of PEMANDU, Dato’ Sri Idris Jala, Malaysia’s tax gap stands at 20%.

Based on the country’s 2014 direct tax collection of RM133.7 billion, a 20% tax gap leaves an estimated (and whopping) RM33 billion worth of taxes uncollected and unaccounted for.

In contrast, many developed nations have tax gaps hovering between 10% – 15%.

Fraud is a major component of a nation’s tax gap, and it comes in many flavors. People and companies evade taxes by underreporting their income. Moreover, they tend to underpay or avoid filing their taxes altogether. The hidden economy – such as money laundering, prostitution, arms and drug trafficking – is also another key reason for a country’s tax gap.

In the past, efforts to widen the field audit and investigation coverage to narrow the tax gap were met with limited success due to manpower constraints and difficulties in monitoring and identifying these fraudulent individuals and organizations.

This is about to change. Governments are increasingly turning to sophisticated techniques like big data analytics to identify tax fraud and increase its tax revenue.

Numerous advanced data analytics tax collection business cases have been developed in recent years. These include initiatives such as propensity to pay, tax collection risk scoring, anti-money laundering, and GST fraud identification.

The Tax Big Data Analytics Maturity Model (Figure 1) shows that high-impact tax business cases that will bring in serious returns for the government – like GST fraud identification, anti-money laundering and real-time fraud detection – will require the deployment of sophisticated analytics that goes beyond traditional reporting, statistical and predictive models. These high-value projects will require the implementation of advanced data analytics such as text mining, path analysis, connections, affinity and visualization.

tax big data maturity model

Figure 1: Tax Big Data Analytics Maturity Model (Image Credit: Teradata)

Let’s take GST scams as an example. The government will be able to use big data analytics to clamp down on companies that charge GST but do not pay the output tax. They will also be able to use advanced analytics techniques to detect GST carousel fraud (Figure 2).

Traditionally, it is impossible for tax fraud analysts to scrutinize millions of transaction data points. The execution of the carousel fraud, for instance, usually takes place in just a few weeks, and by the time it is detected by tax agents, the fraudsters would have gone missing with stolen tax money.

With big data analytics, millions of transactions can be automatically processed. A graph data model can be built to represent GST fraud. Powerful visualization tools can be used to present the chains of suspicious transactions. Fraud analysts can drill down to see companies and individuals connected to these transactions. These suspicious cases can then be flagged for audit. Anti-fraud and investigation teams will be able to act faster to avoid the crime from being committed.

Yep, the taxman is comin’ after you with big data.

gst carousel fraud

Figure 2: An Example of a GST Carousel Fraud (Image Credit: Inland Revenue Authority of Singapore)

 

(Webinar) How SMEs can tap onto AI-driven automation tools for competitive advantage

Make yourself available on this date and find out how you can exploit tools driven by Artificial Intelligence (AI) to gain marketing success and competitive advantage.

Date: 13 Feb 2017 (Monday)
Time: 3.00 – 4.00 pm (MYT / SGT)
Format: Webinar

REGISTER FOR FREE HERE

AGENDA
1. Five most common mistakes SMEs face when it comes to Facebook marketing
2. Future and trends of Facebook marketing in 2017
3. How SMEs can tap onto automation tools driven by AI to gain marketing success and competitive advantage
4. Demo of Optimate product
5. Case Studies / success stories

TARGET AUDIENCE
Retailers, e-commerce owners, e-entrepreneurs and anyone interested in employing AI to enhance their marketing campaigns.

SPEAKER: Dr Wenting Sun is the CEO of Optimate, a high-tech startup focused on marketing AI. Dr Sun is a data scientist, innovator and technopreneur. She has been active in the application and commercialization of data-driven technologies for the last 15 years. She has led Optimate to win multiple awards in the region, and be featured in various media and IAB and IDC events as one of the most innovative AI platforms available for marketers.

AIRVOLUTION 2017: World’s first hackathon by AirAsia

AIRVOLUTION 2017 – the world’s first hackathon by AirAsia aimed at spearheading innovation in the Asean region. The challenge will be open to 100 selected individuals – the cream de la cream of software developers, designers, engineers and/or technopreneurs – who are based in any AirAsia destinations.

AIRVOLUTION 2017 has listed 3 key challenges for the participants to crack:

  • How does one improve customer experience based on their digital footprints?
  • How does one reduce wait times at airports, from check-in to boarding?
  • How does one craft the best itineraries, the best flights and the best tours for AirAsia customers?

Although the Malaysian low-cost airline does not state it upfront, the above 3 challenges are very much data-driven. The hackers will likely be given a snapshot of various datasets from AirAsia, as well as AirAsia BIG and BIG Duty Free, the airline’s loyalty program and online duty free store respectively. These datasets are expected to be available via APIs.

And the solutions will most probably have to be developed in a “mobile-first” spirit and on leading mobile platforms.

The participants had better come equipped with mobile app development skills, as well as proficiencies in data science.

The winning team will receive RM25,000, 100,000 AirAsia Big Points, and 5 return flights to any destination flown by AirAsia.

 

Contact Centres Asia 2017: Driving Contact Centre Transformation

Driving Contact Centre Transformation through Digitisation, Innovation & Exceptional Customer Experience

14 – 16 March 2017
Grand Millenium Kuala Lumpur, Malaysia

Contact centres of today are no longer just about handling enquiries and resolving complaints. The focus is now on transforming contact centres into strategic customer experience management (CEM) assets.

Join your peers at this MUST-ATTEND event to strategise how you can take your contact centres to new heights in 2017! From deploying new digital contact channels, streamlining operational efficiency to investing in the latest technologies such as biometrics, speech analytics, virtual assistants and chat bots, the Contact Centres Asia Summit 2017 will showcase digital transformation opportunities and highlight the latest technology and best practices on enhancing customer interactions.

Download the brochure at: http://bit.ly/2jY8h9H

*Register with discount code: ISG_ADS and enjoy 10% off the standard rate. Simply email enquiry@iqpc.com.sg with your full contact details.

https://contactcentresasia.iqpc.sg

CONTACT DETAILS
Susy A.
Tel: +65 6722 9388
E: enquiry@iqpc.com.sg

Malaysia Airports develops data insights to optimize customer satisfaction

Airport operator company, Malaysia Airports, with interests in Turkey, India, and South Asia, is working with Fusionex on a proof-of-concept: to develop a Business Intelligence platform to further enhance Malaysia Airports’ retailer management system within KLIA and provide value-added services for travelers.

Introduction

Malaysia Airports is an airport management company with a portfolio of almost 40 airports in and around South Asia and Turkey. Apart from the aeronautical business, the client also manages other portfolios including duty-free & retail operations, hotels, free commercial zones, commercial space leasing, and management of parking facilities. It also runs global training centers regarding airport management, airport fire & rescue services, and aviation security.

The Challenge

Malaysia Airports sought to enhance its customer data collection methods and determined to increase the accuracy of gathered information on the performance of its retail units within KLIA. This involves an approximately 400,000 square foot containing retail outlets at various locations including the arrival and departure halls as well as check-in counter areas. These outlets/locations see a fluctuating amount of shoppers at different times and days of the week.

Malaysia Airports aims to ramp up its data collection frequency near the shops area. A higher degree of prompt data collection would serve to accurately determine when promotional activities should be held, or if they were held – what their projected results would be. Accuracy of information garnered from this area is important to determine the customers’ hotspots and their spending trends.

A precise method to track spending trends at the terminal would allow Malaysia Airports to identify local shoppers and international travelers apart. This would also result in a more informed decision being made with regards to what the retail composition should be – whether the terminal should host more shops selling food, garments, souvenirs, or other goods base on the shoppers spending pattern and behaviour.

The Solution

A combination of the Fusionex Business Intelligence platform together with Internet of Things (IoT) devices was utilised to collect and manage the terminal’s retail data. Sensors were installed at the main terminal building’s arrival and departure halls and the contact piers for international and domestic flights.

Tracking the traveler jouney via the Fusionex BI Dashboard

Sensors are also placed in and around the airport’s retail sections to determine hotspots where the most retail activity occurred. The sensors are functioning independently without requiring wiring or lengthy installation methods. Among the sensors used were those with Bluetooth capabilities and Wi-Fi detection capabilities installed in collaboration with solution provider Tapway.

A mobile application was developed to track the basic demographic background of customers including details such as gender and nationality. A campaign was conducted to encourage travelers to sign up for the mobile app. The app could then use the demographic information to craft personalized marketing messages and promotions for the customers.

Using a wide set of connectors, various type of data set and system can be integrated into single platform with speed and ease. The dashboard reporting is then rendered automatic as well, giving the client the ability to view revenues in near real time. These endeavors were supported and co-funded by the Malaysia Digital Economy Corporation (MDEC).

The Benefits

The ability to receive performance data from their existing tenants quickly, together with traveler information from installed sensors, allowed Malaysia Airports to make more accurate decisions:

Understand traveler habits and shopper behavior, preferences. Malaysia Airports will be able to determine travelers’ flow and identify trends of unique shoppers via the installed sensors. Other than that, demographic data such as gender, date of birth and nationality will also be gathered to help Malaysia Airports understands which retail outlets appeal the most to which groups of people. This provides insights into what composition of retail outlets would be optimum in the airport – whether or not certain shops fit, and whether they fit better in domestic or international halls.

Ability to perform more accurate marketing efforts. Passengers’ traffic data gathered, which could be drilled down to daily and even hourly, could inform Malaysia Airports when would be the right time to hold promotional and marketing events within the terminal. Connectivity via travelers’ mobile devices provides a possibility for future marketing undertakings such as customer-specific push notifications. When walking near a particular shop which the traveler might be interested in, the platform pushes a notification to their phones for a promotion on items or services available.

Summary

By utilizing IoT devices and gathering useful data on a centralized system, Malaysia Airports is able to get a good overall view of its airport’s retail sector. Being provided with user-friendly BI dashboards also enable management-level personnel to get quick looks at the information to make timely and accurate data-driven decisions.

Industry: Aviation / Retail

Big Data Solutions Provider: Fusionex is an established multi-award winning IT software group that specializes in Analytics and Big Data. Their business is to help clients manage, make sense of and derive useful insights and information from the vast amounts of structured and unstructured data at their disposal. Fusionex is focused on bridging the gap between business and technology, and in doing so, providing an exceptional and positive experience to customers of various markets.