Digital Finance Question

Part
01
of three
Part
01

Robotic Process Automation and Blockchain

We found ten examples of RPA and blockchain applications for the finance industry. The five RPA case studies we found include KPMG's automation of a client's quarterly financial reporting and IBM's automation of bank reconciliation; the blockchain case studies we found include IBM Global Finance's use of blockchain for dispute resolution and some blockchain applications for international trade finance and supply chain operations. Read on for the full rundown of these case studies.

ROBOTIC PROCESS AUTOMATION CASE STUDIES

KPMG used RPA in 2017 to automate a client's quarterly financial reporting process. The client was creating these reports manually, which was slow and time-consuming; KPMG "robotized" the process, automating data retrieval, PowerPoint generation, and even emailing the report to stakeholders. The program resulted in a time and cost savings of "up to 70 percent" in addition to higher employee satisfaction and greater audit capabilities.

Infosys used RPA to automate insurance claims processes for a "leader in the financial services industry" in 2017. The company automated both financial and nonfinancial claims processing, letting the client cut its processing workforce in half and reduce manual effort devoted to claims processing by 58%.

In 2017, IBM used RPA and Watson AI services to automate the bank reconciliation processes of a client company. The client's reconciliation process was slow and error-prone; RPA improved accuracy by 80% and reduced the time of the reconciliation cycle by 3 days.

Greysoft used RPA to improve automotive loan credential verification at Vahan in India in 2018. Vahan staff were manually entering and retrieving loan customer data, causing frequent errors. Greysoft's RPA bot automated that data entry and retrieval, reducing loan processing time by 40%.

PWC Belgium used RPA to automate several processes in the finance department of a large hospitality company in Belgium in 2017. PWC's RPA service automated the company's daily revenue data entry and reporting across Excel and other applications, resulting in a "50-60% gain in efficiency."

BLOCKCHAIN CASE STUDIES

IBM Global Finance began using blockchain for financial dispute resolution in 2016. IGF has 125,000 clients around the world, and they handle over 25,000 financial disputes a year with an average disputed amount of $31,000 US. They use blockchain to show all parties the full details of the dispute, solving issues more quickly. Blockchain has reduced IGF's dispute resolution times from more than 40 days to less than 10.

IBM has also used blockchain to enhance Maersk's supply chain; the two companies piloted a blockchain program in 2016 and formally announced a partnership in 2017. Together, they hope to use blockchain to increase transparency along the global supply chain, "reduce fraud and errors, [reduce] time products spend in the transit and shipping process, [improve] inventory management and ultimately [reduce] waste and cost."

In 2016, Barclays used blockchain solutions to support a letter of credit transaction between Ornua and Seychelles Trading Company. Basing the LC transaction on the blockchain eliminated a large amount of physical paperwork; future transactions of this nature could "be hugely beneficial in supporting the supply chain, through reduced costs, error-free documentation, and fast transfer of original documents to our customers worldwide."

In 2017 a Japanese conglomerate used blockchain to facilitate an international finance transaction with an Australian company. The companies carried out the entire trade process on the blockchain, "from issuing a letter of credit to delivering trade documents," increasing the transparency and speed of the trade.

Emirates NBD and ICICI Bank piloted blockchain technology for "trade finance and international remittances" between the UAE and India in 2017. The two banks used blockchain for bank-to-bank transaction initiation, document sharing, and messaging. This improved their transparency, reduced the need for paper in transactions, and enabled the two banks to track and share documents more easily.

This article contains several more use cases for blockchain in finance, including Nasdaq's use for digital share ownership representation, Barclays and other banks' creation of an international forex settlements consortium on the blockchain, and Royal Bank of Canada's use of blockchain for international funds transfer to the US. The article doesn't go into enough detail for these to really be considered case studies, however.

CONCLUSION

We've assembled a list of five RPA case studies in finance, including PWC Belgium's automation of a hospitality client's revenue reporting processes and Greysoft's use of RPA to automate Indian lender Vahan's client data entry and retrieval processes. We've also assembled five case studies of blockchain applications for finance, including IBM Global Finance's use of blockchain for financial dispute resolution and Emirates NBD's partnership with ICICI Bank to use blockchain for remittance, document sharing, and bank-to-bank messaging.
Part
02
of three
Part
02

Natural Language Generation, Cognitive Analytics

The use of Natural Language Generation/Processing and cognitive analysis technology in the finance industry is prevalent and proving to be invaluable. Banks and other financial institutions are utilizing this technology to create client profiles, target customers for specific products, and even combat against technological crimes. Below you will find five case studies of financial institutions across the globe that are utilizing this technology, and how it has positively impacted their business actions.

Troy Asset Management

Located in the United Kingdom, Troy Asset Management began using Quill, an NLG Platform, for Asset Management. Troy went into business with Narrative Science, the producer of Quill, with the goal of increasing their presence in the media to secure more national and top-end investors. Quill for Asset Management specifically works to write client and account portfolios, in addition to providing commentary on such accounts. Troy used this functionality to analyze portfolios of current clients in addition to current news media sources as a way to understand the market and prospective clients. As a result, Troy Asset Management has been mentioned across multiple news sources, including The New York Times, and has been able to establish new relationships with fund managers.

USAA

On November 28, 2014, USAA Bank also entered into an agreement with Narrative Science to utilize Quill - the same technology used by Troy Asset Management. USAA Bank, however, utilized Quill in their investor relations sector. Quill created portfolios for all of the 10,000 plus members of USAA bank, which are then used to provide financial advise for clients in addition to information that can be used by bank personnel and customers alike.

London Stock Exchange

The London Stock Exchange is currently utilizing an AI-based monitoring system from SparkCognition for market analysis purposes. The technology is loaded with information regarding current methods for manipulating the stock market, in addition to ways to guard against these known methods. The system also has the capability to learn from this information and combat against new forms of market manipulation, from which it can then alert analysts about the potential abuse. This technology has already worked to alert officials about the London Interbank Offer Rate (LIBOR) rigging scandal.

Credito Valtellinese

Credito Valtellinese is an Italian bank that has begun working with IBM and their cognitive analysis tools for marketing management solutions. The bank's internal service unit, Creval Sistemi e Servizi (CSS) began using IBMs technology to identify, capture, and index free-text data within the company's database. Information is gathered not only from internal records, but also from financial records, banking products owned by customers (whether from the bank or another source), shopping habits, and personal financial arrangements of individuals customers. With this information, IBMs technology utilizes Natural Language Processing (NLP) to create customized profiles for clients that can be further used to identify customers and market specific products to them based on the information compiled. As a result, Credito Valtellinese has seen a 10% increase in outbound marketing conversion rates, a 2% increase in revenue-per-customer, and greater overall efficiency in the marketing and analytical departments.

ANZ Bank

Since May 2013, ANZ Bank from Australia has been utilizing two different forms of technology to optimize customer service from financial advisers and risk scoring and pricing processes. ANZ Bank uses IBM's Watson to help identity services and products that the bank offers specific to a customers needs and experiences. The technology is connected with disclosure statements, market data, financial statements, terms and conditions of wealth products, and client information, all of which is used to target products specifically to customers. As of 2016, ANZ Bank also is using technology from Experian to increase the automation in the unsecured and personal loans sector. This technology is self-learning and analyzes client profiles to approve or disapprove of them for loans. As a result, approval times have decreased by 5%, 150,000 additional clients are receiving immediate responses, and 1000 hours of back-office work has been eliminated.

Conclusion

Natural Language Processing and automated technology are helping financial institutions to organize customer information and provide information regarding business processes. The most widely used function of this technology is to create client profiles, but the ways in which these profiles are utilized range greatly. The availability and presence of this technology in the financial industry is likely to grow in coming years, as the time to complete actions with this technology decreases and business grows.
Part
03
of three
Part
03

Predictive Analytics

We found case studies about the use of predictive analytics by the Bank of Ireland, Taishin Bank, Aviva, and Monext. Although the case studies included within this request do not directly specify which finance function of each company benefited the most from the adoption of this technology, we found that departments involved include the retail marketing strategy department of the Bank of Ireland, the ATM department and the customer calls and profiling department of Taishin Bank, the marketing campaign strategy and customer calls department of Aviva, and the customer experience and call center departments of Monext.

BANK OF IRELAND

Presidion is a Dublin-based leading provider of analytics services and solutions within Ireland and the UK. They recently published several case studies about financial institutions that have adopted predictive analytics solutions and services to improve specific financial functions. One of the case studies covers how Presidion helped the retail marketing strategy department of the Bank of Ireland.
PROBLEM
The Bank of Ireland recently decided to implement a new customer incentive program. The challenge is the fact that the program requires the compilation of over thousands of records about the sentiments of all their customers. In order to avoid unnecessary spending of both resources and work hours, the Bank of Ireland needed a more efficient way of compiling all of such information.
SOLUTION
After evaluating other suppliers, The Bank of Ireland ultimately decided to adopt Presidion’s IBM SPSS analytics solutions. The abbreviation SPSS stands for the original name of their product, Statistical Package for Social Sciences, back when the company was founded. Today, the name SPSS is retained to represent the company’s line of predictive analytical solutions.
Presidion’s IBM SPSS text analysis for surveys was able to assist the retail marketing strategy department of the Bank of Ireland in analyzing over 20,000 text files which contain information about customer sentiments. The process involved both reading and identifying the content of each file before identifying recurring key issues through predictive analytics.
OUTCOME
As a result, The Bank of Ireland’s retail marketing strategy department was able to significantly cut-down on time spent collating data, allowing them to allocate more time and resources for “driving insights out of data”.

TAISHIN BANK

Gartner recently compiled several case studies of companies that have used AI and other analytical solutions such as predictive analytics. One of the case studies revolves around how Taishin Bank in Taiwan's departments in charge of ATMs and customer calls and profiles benefited from this technology.
PROBLEM
Taishin Bank was looking for a way to reduce costs by more efficiently analyzing and compiling customer data from all their channels.
SOLUTION
In light of this, Taishin Bank decided to create the Opportunity-Based Call Distribution (OBCD) system. The OBCD analyzes all incoming calls and determines whether to divert customers to either regular or specialized representatives based on certain attributes. The initiative brought back positive results and the company has since applied the same strategy to their other channels such as their ATMs.
OUTCOME
As a result, Taishin Bank saved over 47 million NTD through the use of predictive analytics. The OBCD also helped reform the bank’s workflow for personal loan applications.

AVIVA

SAP is a globally-recognized provider of enterprise application software. They recently compiled a report of multiple customer success stories from companies that used their predictive analytics solutions. One of the case studies revolved around how Aviva's departments in charge of marketing campaign strategies and customer calls and profiling benefited from this technology.
Founded back in 1696, Aviva is a UK-based insurance company with over 33 million customers throughout Europe.
PROBLEM
Aviva found it inefficient to keep building generic models for every customer they entertain. In addition to this, they also found themselves contacting customers too often. Lastly, the company wanted to both more efficiently identify customers that were most likely to respond to their marketing campaigns and also increase response rates.
SOLUTION AND OUTCOME
Through SAP predictive analytics solutions, Aviva was able to create over 30 propensity models for entire customer groups. The company was also able to create more-personalized offers which led to an increase in customer lifetime value and campaign response rates. Overall, Aviva was able to more efficiently keep track of trends within the industry through the collection and analysis of the latest incoming data.

MONEXT

Monext is a France-based provider of “Payment and money card processing solutions and services”. SAP's compilation also included a case study about how Monext's departments for customer experience and call centers benefited from predictive analytics.
PROBLEM
The concerns of Monext revolved primarily around the increasing cases of e-fraud in Europe. Aside from the need to implement a system that could more efficiently predict E-fraud, the company also wanted to improve consumer experience and reduce call center costs by greatly reducing false alerts.
SOLUTION AND OUTCOME
Through SAP predictive analytics solutions, Monext was able to create a customized predictive model for differing card providers in half the time they would normally need to tackle such issues. In addition to this, the company was able to further tailor predictive models depending on the type of card provided. SAP predictive analytics solutions also allowed Monext to analyze over 500 derived and native attributes, used for the evaluation of electronic transactions, within only a few milliseconds.

CONCLUSION

The Bank of Ireland, Taishin Bank, Aviva, and Monext have all used predictive analytics technologies to improve the performance of finance functions such as retail marketing strategy departments, ATM departments, customer calls and profiling departments, and consumer experience and call center departments.
Sources
Sources