Insurtech in Europe: identifying the top investment priorities for driving innovation
Tóm tắt
The purpose of this study is to determine the essential indicators to improve insurtech systems and select the most critical alternative to increase insurtech-based investments in European countries. A novel fuzzy decision-making model is generated by integrating entropy and additive ratio assessment (ARAS) techniques with spherical fuzzy sets. First, the indicators are weighted using spherical fuzzy entropy. Then, the alternatives are ranked using spherical fuzzy ARAS. The alternatives are also ranked with the spherical fuzzy technique for order of preference by similarity to the ideal solution methodology. The main contribution of this study is that it would help investors to take the right actions to increase the performance of insurtech investments without incurring high costs. Another important novelty is that a new fuzzy decision-making model is proposed to solve this problem. The results of the two models are quite similar, proving the validity and coherency of the findings. It is found that pricing is the most critical factor that affects the performance of insurtech investments. Insurtech companies are required to make accurate pricing by conducting risk analyses to increase their profits and minimize their risks. Additionally, according to the ranking results, big data are the most appropriate way to improve the performance of insurtech investments in Europe. Big data analytics helps companies learn more about the behavior of their customers. By analyzing data about their customers’ past transactions, companies can provide more convenient services to them. This would increase customer satisfaction and enable companies to achieve long-term customer loyalty.
Tài liệu tham khảo
Abbas A, Bilal K, Zhang L, Khan SU (2015) A cloud based health insurance plan recommendation system: a user centered approach. Future Gener Comput Syst 43:99–109
Adem A, Çakıt E, Dağdeviren M (2022) A fuzzy decision-making approach to analyze the design principles for green ergonomics. Neural Comput Appl 34:1–12
Agarwal S, Bhardwaj G, Saraswat E, Singh N, Aggarwal R, Bansal A (2022) Insurtech fostering automated insurance process using deep learning approach. In: 2022 2nd International conference on innovative practices in technology and management (ICIPTM), vol 2. IEEE, pp 386–392
Awais M, Afzal A, Firdousi S, Hasnaoui A (2023) Is fintech the new path to sustainable resource utilisation and economic development? Resour Policy 81:103309
Aydoğdu A, Gül S (2022) New entropy propositions for interval-valued spherical fuzzy sets and their usage in an extension of ARAS (ARAS-IVSFS). Expert Syst 39(4):e12898
Balasubramanian R, Libarikian A, McElhaney D (2018) Insurance 2030—the impact of AI on the future of insurance. McKinsey & Company, Atlanta
Barukab O, Abdullah S, Ashraf S, Arif M, Khan SA (2019) A new approach to fuzzy TOPSIS method based on entropy measure under spherical fuzzy information. Entropy 21(12):1231
Basse T, Reddemann S, Rodriguez Gonzalez M (2022) Dividend signaling or dividend smoothing? New empirical evidence from the italian insurance industry after the global financial crisis. Z Gesamte Versicherungswissenschaft 111:1–22
Bittini JS, Rambaud SC, Pascual JL, Moro-Visconti R (2022) Business models and sustainability plans in the FinTech, InsurTech, and PropTech industry: evidence from Spain. Sustainability 14(19):12088
Borselli A (2020) Smart contracts in insurance: a law and futurology perspective. Springer, Berlin, pp 101–125
Cai L, Firdousi SF, Li C, Luo Y (2021) Inward foreign direct investment, outward foreign direct investment, and carbon dioxide emission intensity-threshold regression analysis based on interprovincial panel data. Environ Sci Pollut Res 28:1–14
Cao S, Lyu H, Xu X (2020) InsurTech development: evidence from Chinese media reports. Technol Forecast Soc Change 161:120277
Chang VY (2023) Technology investments and firm performance under the wave of InsurTech. Geneva Pap Risk Insur Issues Pract. https://doi.org/10.1057/s41288-023-00286-w
Chatzara V (2020) FinTech, InsurTech, and the regulators. In: Marano P, Noussia K (eds) InsurTech: a legal and regulatory view. Springer, Cham, pp 3–25
Che X, Liebenberg A, Xu J (2022) Usage-based insurance—Impact on insurers and potential implications for InsurTech. N Am Actuar J 26(3):428–455
Christofilou A, Chatzara V (2020) The internet of things and insurance. In: Marano P, Noussia K (eds) InsurTech: a legal and regulatory view. AIDA Europe research series on insurance law and regulation, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-030-27386-6_3
Cortis D, Debattista J, Debono J, Farrell M (2019) InsurTech. In: Lynn T, Mooney JG, Rosati P, Cummins M (eds) Disrupting finance: FinTech and strategy in the 21st century. Springer, Cham, pp 71–84
Dahiya M, Sharma S, Grima S (2022) Big data analytics application in the Indian insurance sector. In: Sood K, Balusamy B, Grima S, Marano P (eds) Big data analytics in the insurance market. Emerald Publishing Limited, Bingley, pp 145–164
Dizaji AK, Payandeh Najafabadi AT (2022) Updating bonus-malus indexing mechanism to adjust long-term health insurance premiums. N Am Actuar J. https://doi.org/10.1080/10920277.2022.2110123
Eckert C, Neunsinger C, Osterrieder K (2022) Managing customer satisfaction: digital applications for insurance companies. Geneva Pap Risk Insur Issues Pract 47(3):569–602
Ege MS, Stuber SB (2022) Are auditors rewarded for low audit quality? The case of auditor lenience in the insurance industry. J Account Econ 73(1):101424
Eling M, Nuessle D, Staubli J (2021) The impact of artificial intelligence along the insurance value chain and on the insurability of risks. Geneva Pap Risk Insur Issues Pract 47:1–37
Farbmacher H, Löw L, Spindler M (2022) An explainable attention network for fraud detection in claims management. J Econom 228(2):244–258
Feng S, Wang W, Xiong Z, Niyato D, Wang P, Wang SS (2018) On cyber risk management of blockchain networks: a game theoretic approach. IEEE Trans Serv Comput 14(5):1492–1504
Fursov I, Kovtun E, Rivera-Castro R, Zaytsev A, Khasyanov R, Spindler M, Burnaev E (2022) Sequence embeddings help detect insurance fraud. IEEE Access 10:32060–32074
Gatzert N, Schubert M (2022) Cyber risk management in the US banking and insurance industry: a textual and empirical analysis of determinants and value. J Risk Insur 89(3):725–763
Gocer F, Sener N (2022) Spherical fuzzy extension of AHP-ARAS methods integrated with modified k-means clustering for logistics hub location problem. Expert Syst 39(2):e12886
Gomes C, Jin Z, Yang H (2021) Insurance fraud detection with unsupervised deep learning. J Risk Insur 88(3):591–624
Ho CM (2023) Research on interaction of innovation spillovers in the AI, Fin-Tech, and IoT industries: considering structural changes accelerated by COVID-19. Financ Innov 9(1):7
Jin Y, Ashraf S, Abdullah S (2019) Spherical fuzzy logarithmic aggregation operators based on entropy and their application in decision support systems. Entropy 21(7):628
Kayacık M, Dinçer H, Yüksel S (2022) Using quantum spherical fuzzy decision support system as a novel sustainability index approach for analyzing industries listed in the stock exchange. Borsa Istanbul Rev 22(6):1145–1157
Kimberly P, Grima S, Özen E (2022) Perceived effectiveness of digital transformation and insurtech use in malta: a study in the context of the European Union’s green deal. In: Sood K, Dhanaraj RK, Balusamy B, Grima S, Uma Maheshwari R (eds) Big data: a game changer for insurance industry. Emerald Publishing Limited, Bingley, pp 239–263
Kou G, Olgu Akdeniz Ö, Dinçer H, Yüksel S (2021) Fintech investments in European banks: a hybrid IT2 fuzzy multidimensional decision-making approach. Financ Innov 7(1):39
Kou G, Yüksel S, Dinçer H (2022) Inventive problem-solving map of innovative carbon emission strategies for solar energy-based transportation investment projects. Appl Energy 311:118680
Kou G, Dincer H, Yuksel S, Alotaibi FS (2023) Imputed expert decision recommendation system for QFD-based omnichannel strategy selection for financial services. Int J Inf Technol Decis Mak. https://doi.org/10.1142/S0219622023300033
Lee CY, Tsao CH, Chang WC (2015) The relationship between attitude toward using and customer satisfaction with mobile application services: An empirical study from the life insurance industry. J Enterp Inf Managt 28:680–697
Leiria M, Rebelo E, deMatos N (2022) Measuring the effectiveness of intermediary loyalty programmes in the motor insurance industry: loyal versus non-loyal customers. Eur J Manag Bus Econ 31(3):305–324
Li C, Firdousi SF, Afzal A (2022a) China’s Jinshan Yinshan sustainability evolutionary game equilibrium research under government and enterprises resource constraint dilemma. Environ Sci Pollut Res 29(27):41012–41036
Li Y, Kou G, Li G, Hefni MA (2022b) Fuzzy multi-attribute information fusion approach for finance investment selection with the expert reliability. Appl Soft Comput 126:109270
Liu J, Ye S, Zhang Y, Zhang L (2023) Research on InsurTech and the technology innovation level of insurance enterprises. Sustainability 15(11):8617
Ma QX, Zhu XM, Bai KY, Zhang RT, Liu DW (2023) A novel failure mode and effect analysis method with spherical fuzzy entropy and spherical fuzzy weight correlation coefficient. Eng Appl Artif Intell 122:106163
Marafie Z, Lin KJ, Zhai Y, Li J (2018) Proactive fintech: using intelligent iot to deliver positive insurtech feedback. In: 2018 IEEE 20th conference on business informatics (CBI), vol 2. IEEE, pp 72–81
Menekşe A, Camgöz Akdağ H (2022) Seismic vulnerability assessment using spherical fuzzy ARAS. In: Intelligent and fuzzy techniques for emerging conditions and digital transformation: proceedings of the INFUS 2021 conference, held August 24–26, 2021, vol 2. Springer, pp 733–740
Mirza N, Umar M, Afzal A, Firdousi SF (2023) The role of fintech in promoting green finance, and profitability: evidence from the banking sector in the euro zone. Econ Anal Policy 78:33–40
Nai W, Yang Z, Wei Y, Sang J, Wang J, Wang Z, Mo P (2022) A comprehensive review of driving style evaluation approaches and product designs applied to vehicle usage-based insurance. Sustainability 14(13):7705
Naji Alwerfali HS, Al-qaness AA, Abd Elaziz M, Ewees AA, Oliva D, Lu S (2020) Multi-level image thresholding based on modified spherical search optimizer and fuzzy entropy. Entropy 22(3):328
Nayak B, Bhattacharyya SS, Krishnamoorthy B (2019) Integrating wearable technology products and big data analytics in business strategy: a study of health insurance firms. J Syst Inf Technol 21:255–275
Njegomir V, Demko-Rihter J (2023) InsurTech: new competition to traditional insurers and impact on the economic growth. Digital transformation of the financial industry: approaches and applications. Springer, Cham, pp 133–150
Nunes B (2018) Behavioural design and price optimization in InsurTech. In: The VanderLinden SL, Millie SM, Anderson N, Chishti S (eds) InsurTech Book: the insurance technology handbook for investors, entrepreneurs and FinTech visionaries. Wiley, Hoboken, pp 165–170
Özdemirci F, Yüksel S, Dinçer H, Eti S (2023) An assessment of alternative social banking systems using T-Spherical fuzzy TOP-DEMATEL approach. Decis Anal J 6:100184
Pareek T, Sood K, Grima S (2022) The impact of big data technology on the advancement of the insurance industry. In: Sood K, Balusamy B, Grima S, Marano P (eds) Big data analytics in the insurance market. Emerald Publishing Limited, Bingley, pp 221–239
Park K, Wong HY, Yan T (2023) Robust retirement and life insurance with inflation risk and model ambiguity. Insur Math Econ 110:1–30
Ricci O, Battaglia F (2021) The development of InsurTech in Europe and the strategic response of incumbents. In: King T, Lopes FS, Srivastav A, Williams J (eds) Disruptive technology in banking and finance: an international perspective on FinTech. Palgrave Macmillan, London, pp 135–160
Riikkinen M, Saarijärvi H, Sarlin P, Lähteenmäki I (2018) Using artificial intelligence to create value in insurance. Int J Bank Mark 36:1145–1168
Rjoub H, Adebayo TS, Kirikkaleli D (2023) Blockchain technology-based FinTech banking sector involvement using adaptive neuro-fuzzy-based K-nearest neighbors algorithm. Financ Innov 9(1):1–23
Saeed M, Arshed N (2022) big data analytics adoption in the indian insurance industry: challenges and solutions. In: Sood K, Balusamy B, Grima S, Marano P (eds) Big data analytics in the insurance market. Emerald Publishing Limited, Bingley, pp 81–102
Saxena S, Kumar R (2022) The impact on supply and demand due to recent transformation in the insurance industry. Mater Today Proc 56:3402–3408
Seth P, Gulati K (2022) Use of wearable and health applications in insurance industry using internet of things and big data. In: Sood K, Dhanaraj RK, Balusamy B, Grima S, Uma Maheshwari R (eds) Big data: a game changer for insurance industry. Emerald Publishing Limited, Bingley, pp 1–13
Sosa I, Montes Ó (2022) Understanding the InsurTech dynamics in the transformation of the insurance sector. Risk Manag Insur Rev 25(1):35–68
Stoeckli E, Dremel C, Uebernickel F (2018) Exploring characteristics and transformational capabilities of InsurTech innovations to understand insurance value creation in a digital world. Electron Mark 28:287–305. https://doi.org/10.1007/s12525-018-0304-7
Sun RT, Garimella A, Han W, Chang HL, Shaw MJ (2020) Transformation of the transaction cost and the agency cost in an organization and the applicability of blockchain—a case study of peer-to-peer insurance. Front Blockchain 3:24
Tladinyane L, Gumede L, Bick G (2022) Pineapple: the growth challenges faced by a South African Insurtech disruptor. The Case Writing Centre, University of Cape Town, Graduate School of Business, Cape Town, pp 1–16
Unal Y, Temur GT (2022) Sustainable supplier selection by using spherical fuzzy AHP. J Intell Fuzzy Syst 42(1):593–603
Walker T, Nikbakht E, Kooli M (2023) Fintech and banking: an overview. In: Walker T, Nikbakht E, Kooli M (eds) The fintech disruption: how financial innovation is transforming the banking industry. Springer, Berlin, pp 1–8
Wang Y, Xu W (2018) Leveraging deep learning with LDA-based text analytics to detect automobile insurance fraud. Decis Support Syst 105:87–95
Xu X, Zweifel P (2020) A framework for the evaluation of InsurTech. Risk Manag Insur Rev 23(4):305–329
Yuan Q, Zhang X, Zhu H, Zhang B, Chen J (2023) Research on influencing factors of coal mine safety production based on integrated fuzzy DEMATEL-ISM methods. Energy Sources Part A Recov Util Environ Effects 45(1):2811–2830
Yüksel S, Dinçer H (2023) Sustainability analysis of digital transformation and circular industrialization with quantum spherical fuzzy modeling and golden cuts. Appl Soft Comput 138:110192
Yüksel S, Dinçer H, Eti S, Adalı Z (2022) Strategy improvements to minimize the drawbacks of geothermal investments by using spherical fuzzy modelling. Int J Energy Res 46(8):10796–10807
Zarifis A, Cheng X (2022) A model of trust in Fintech and trust in Insurtech: how artificial intelligence and the context influence it. J Behav Exp Finance 36:100739
Zhao Y, Wei S, Du H, Chen X, Li Q, Zhuang F, Kou G (2022) Learning Bi-typed multi-relational heterogeneous graph via dual hierarchical attention networks. IEEE Trans Knowl Data Eng. https://doi.org/10.1109/TKDE.2022.3220520
Zhou YM, Yang W, Ethiraj S (2022) The dynamics of related diversification: evidence from the health insurance industry following the Affordable Care Act. Strateg Manag J 44:1753–1779