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Automation, AI and Analytics in Financial Services

Automation, AI and Analytics in Financial Services
November 21, 2018

Financial service sector has seen unprecedented change in terms of business processes management, regulations, and compliances. Rapid transitions in customer expectations, increasing costs of operations, rising cyber threats and fraud cases in financial transactions have been digging grave, impacting top and bottom line of the business in financial analytics industry. As per 2018 Mckinsey Report, “total loss in card fraud across globe has been reported about $ 23 billion in 2016 and is expected to climb to $ 44 billion by 2025. “ Along with fraudulent transactions, there have been reported cases of cyber threats .In fact in 2018 PWC CEO survey, it is stated that 40% of the CEOs are concerned about cybersecurity threats in financial world. This highlights the intensity of situation. Thus, a huge focus and panacea is needed to root out the challenges. Thanks to evolution of next gen wave of automation, AI and analytics which has potential to wipe out intricacies and challenges in conjunction with business process management.

Time to move to next-gen technological wave in finance world. Read the blog to know how. #automation #artificialintelligence

Shifting to next-gen technological wave

Finance sector has been the most vulnerable, where cases of cyberattacks and fraudulent transactions are innumerous. This has instigated leaders to think beyond in terms of technological advancement to combat the new digital threats and challenges along with meeting regulations and compliances. Business critical factors have to be mapped to next-gen technological KPIs of the industry, which in-turn requires assessment of the industry processes. Looking from the angle of application of advanced technological wave in processes, the industry is well poised for next-gen automation and AI technologies. Thanks to heaps of data which provides it advantage to arrive at business modelling decisions through analytics and implementing the ways to improve the business processes and managing risks. Infact early adopters of next gen automation, AI and advance analytics in the segment have started reaping the benefits in terms of boost in revenues, enhanced customer experience, and reduced cost of operations. Enlisted below are few testimonials which justify the need.

Snapshot of a few testimonials:

  • Adoption of Big Data by CITIC bank in 2010 has boosted credit line of business. It has helped the bank make precise decisions through multi-dimensional approach, resulting in even rejection of some qualified customers due to high risks level.
  • In 2017, Chase bank has automated their teller process, thus reducing overall employee expense by 25%.
  • One of the leading banks in UK has revamped its claim process through deployment of 85 bots which redefined 13 processes, resulting in increased capacity of handling 1.5 million requests per year.
  • One of the leading banks in UK has been able to recover 95% of the loss due to fraud after applying advanced analytics solutions.
  • Morgan Stanley has been able to augment its 16,000 financial advisers through the application of machine learning algorithms which takes over routine tasks and suggests trade as well.

Compared to other sectors where 64% of the organizations have bolstered customer satisfaction after applying intelligent automation (which is a combination of automation and artificial intelligence) in their line of businesses, only 35% of the financial services have adopted intelligent automation and witnessed an increase in 2-3% revenue. Thus, plethora of financial organizations still have to adopt the new techniques of automation.

Putting lenses on key areas

There is no single point of doubt that physics of finance industry involves some of the complex processes and procedures, which are extremely critical in making business decisions. They have always been under radar of multiple stakeholders, be it the customer, service providers, or legal institutions. Changing regulations and compliance has further complicated the case. Thus, there is a compelling need to adopt to newer ways of operations, which are driven by machines and intelligent automation and delivers exceptional results with quick turnaround time. In fact, in 2017 Mckinsey report it has been reported that, “Machines will do up to 10 to 25 percent of work across bank functions, increasing capacity, and freeing employees to focus on higher-value tasks and projects “.

Few potential areas where financial services can derive benefits from AI and analytics are:

Risk management:

In the field of risk management, Big data analytics can play a bigger role in harnessing and make predictions. It can apply predictive modelling technique to improvise response time, identify patterns of customer behaviour, and can classify high and low risk areas.

Fraud detection and claims management:

To address the fraud cases, analytical tools and AI can start with basic step of collecting data from various functions. Artificial intelligence platform can then start monitoring and learning the user’s behavioural patterns, which helps in curbing potential fraud transactions. Fund flow analytics solutions can be applied, as well, to reduce false negative situations, where fraudulent transactions are treated as real and positive situations and real transactions are treated as threat and fraud. Claim management processes, too, can be automated with machine learning techniques, thus reducing turnaround time of claim settlement and enhancing customer experiences.

Regulatory and compliance:

Landscape of changing regulations is being a challenge for financial institutions. AI can apply appropriate methods of learning, remembering and complying with all laws be it KYC laws, anti- money laundering regulations or asset management governance laws.

Customer engagement:

Customer engagement is one of key areas for businesses to focus. Implementation of AI bots have been instrumental in engaging with customers at no less than human interaction level, addressing customer concerns and requests.

One of the ways of engaging with customer through AI has been voice response system. Although it is quite prevalent in finance industry, but still it has to spread its tentacles all over finance world. In fact, per one of senior editors in payment industry, in next 10 years, it is expected that more than 50% of the customer interactions will be through voice. So, there is bigger role of companies in adopting the technological advancement of voice system, thus enhancing customer experiences.

Talking automation without mentioning of Robotic Process Automation (RPA) will render the whole concept incomplete. RPA has been playing a vital role in automating business processes and with a number of evidences along with use cases of successful implementation, leading to huge benefits. Listed below are functions within finance services where implementation of RPA, in synergy with business process management, has automated the whole ecosystem:

  • Procure to pay- Invoices intake through BPM and assigning it to the right workforce
  • Order to cash- Analysis of sales quotes, validation of sales order with the help of bots
  • Record to report- Reconciliation of accounts, collaboration, and management of nter- company transactions

Few other broad areas where Automation, AI and Analytics can play substantial role are:

  • Dispute Management
  • Credit and Debit Card application
  • Processing of Exceptions
  • Statement reconciliation

The right approach:

Legacy framework will not be effective in addressing challenges in the industry. It is imperative to adopt next-gen intelligent automation and apply 360-degree approach which should envision the future state. To make it successful, it is extremely important to identify areas where automation and analytics can be applied.

“The first rule of any technology used in a business is that automation applied to efficient operation will magnify the efficiency. The second rule is that automation applied to inefficient operations will magnify the inefficiency “- Bill Gates

Having said that, center of excellence (COE) needs to be set up, which can put together transformative roadmap of redesigning processes along with evaluating and selecting right IT partners for end-to-end implementation.

Agenda on a business table:

There is no business which is successful without giving thought of reducing cost and increasing revenues. Applying automation in the field of finance can significantly impact the top line business of the organization.

In fact, in one of the recent Capgemini Digital Transformation report 2018, it has been reported that by 2020, the financial services industry could scale global revenues up to $512 billion through intelligent automation.

Further, according to 2016 EY survey, 57% of CFOs believe that implementation of predictive analytics is important in future, considering transitions in regulations and compliances.

With industries adopting next wave of technology, it is time for business leaders to think and put a roadmap for implementation of intelligent automation. They need to harness the power of data and machine learning techniques, contributing to the top line of the business. Predictive analytics running on large set of data can provide different views which help in taking accurate decisions in areas of risk modelling, customer engagement and business process management. For transcending to next technological wave, identification of right IT service provider having rich expertise in proving engineering solutions around analytics, business process mapping and intelligent automation is paramount.

Organization structure change strategy should be on the cards of business leaders. With the implementation of AI and automation, there will be need to redeploy or cut off existing workforce. To cater to those transition within and outside the function, they should lay out proper plan of re-skilling workforce on new platform or cutting off from the function. Role of HR leaders becomes critical in transforming the workforce skills that should co-exist with artificial & machine learning skill in two- way learning relationship. While managing transitions of workforce, transparency has to be shown by the organization for its approach. Along with redefining organization structure, they have to focus on recruiting new set of talent who is eager to learn and apply intelligent automation techniques.

The road ahead:

While industry is in a transformation mode to new avatar, application of legacy framework will be ineffective to address the new changes. Change champions will have to play a vital role there through implementing the right approach scalable enough to meet business needs. Early adopters of technology will be at advantageous position compared to the ones skeptical of implementing automation and artificial intelligence in finance.

Future lies in generating alpha in the business process which is possible through:

  • Identification and assessment of potential processes for automation
  • Running a pilot model before implementation on large scale
  • Setting up of COE which governs the transformation approach
  • Organization change management taking care of workforce transition

We need to remember words of Ray Kurzweil, one of the renowned American author, computer scientist and futurist. He stated “In 2045 non-biological intelligence will be one billion times more powerful than human intelligence today “

The need of the hour is to follow new wave of technological innovation, thus heading towards the path of improving the whole ecosystem through intelligent automation and advance analytics in business world of finance.